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  • arXiv cs.LGOct 20

    The Dynamics of Collapse and Recovery in Clustered Federated Learning

    Priyanka Sinha, Snigdhansu Chatterjee

    Clustered federated learning (CFL) can suffer from cluster collapse. In particular, under unfavorable initialization, early client assignments concentrate heterogeneous groups on one cluster model, which is then trained toward a pooled compromise, while another model receives no updates. Since the current models determine client routing and routing determines subsequent model updates, we formulate clustered federated learning as control of an unknown dynamical system, with cluster models as the evolving state, client routing patterns as the control input, and routing interventions as state dependent controllers. In a two group linear model, we identify an initialization region in which greedy routing makes collapse self reinforcing. We then derive finite horizon error dynamics showing how routing interventions can reverse collapse by repeatedly training the starved model, primarily with updates from its compatible group. Motivated by this condition, each client uses blocked EXP3 to choose between greedy routing and interventions that redirect updates toward unused models. Experiments show that with simpler predictors and faster specialist formation, autonomous controller…

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  • arXiv cs.LGOct 20

    Discount Yourself: Self-inclusive Freshness-weighted Gossip for Decentralised Learning under Client Outages

    Qiyuan Wang, Ke Xiao, Christos Anagnostopoulos

    Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real devices, however, are often offline for hours or days, and a client returning from an outage can recover only by aggregating the models its neighbourhood holds. We observe that the stalest of these models is usually the client's own, a case that existing staleness-aware rules, which discount only the models a client receives, do not address. We formalise aggregation after an outage as estimating the state of a fully available network and derive three principles. First, the receiver must be discounted like any neighbour. Every rule that weights it at least as much as each neighbour has an error that grows without bound with the outage, whereas discounting all candidates by relative staleness keeps the error bounded. Second, staleness must be measured as inherited lag rather than age, since local training resets a model's age but not the lag it absorbed from stale neighbours. We introduce the effective lag, which tracks this quantity at the cost of one scalar per message. Third, the discount must be steep, because absorbed lag…

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  • arXiv cs.LGOct 20

    Coincidence-Maximizing Customer Clustering Using Smart Meter Data

    Ramiro H. Gálvez, Gabriel Martos Venturini, Iván Mondrzak

    Electricity distribution planning depends on peak-demand coincidence within customer groups, yet similarity-based clustering of smart-meter data does not optimize it directly. We formulate customer grouping to maximize the average of group coincidence factors, weighted by each group's daily share of summed individual peak demand. We prove that jointly optimizing memberships and group peak times is strongly NP-hard even for two groups. We therefore develop a fixed-peak refinement procedure applicable to any feasible partition. It alternates between identifying group peak times and exactly reassigning customers under minimum group-size constraints. Each polynomial-time reassignment maximizes a lower bound tight at the current partition, so accepted updates strictly improve weighted coincidence. We initialize refinement by clustering individual peak-time distributions and evaluate the method on four public smart-meter panels (27,654 customers) using chronological holdouts and 200 paired replicates with complete refitting. Refinement consistently improves weighted coincidence during fitting and reduces sensitivity to initialization. On held-out days, the method outperforms random…

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  • arXiv cs.CROct 20

    Contextual Candour: Role-Aware Graduated Disclosure for LLMs over Mixed-Sensitivity Contexts

    Devansh Srivastav, David Pape, Samira Abedini, Lea Schönherr

    LLMs increasingly operate over mixed-sensitivity contexts, where they must reason over information ranging from public to confidential while disclosing only what the user is authorised to receive. Existing approaches enforce access control as a binary decision: either fully answering or refusing, without regulating how much or what kind of information is revealed if access is granted, leading to over-refusal or unintended leakage. We introduce graduated disclosure, an access-control formulation in which the level of detail and sensitivity in a model's response is calibrated to the user's authorisation rather than treated as a grant-or-deny decision. We model graduated disclosure as a disclosure-aware generation problem and propose a reward-based optimisation framework that shapes responses toward level-appropriate abstractions while penalising unauthorised factual leakage, including paraphrases and approximations. We evaluate our approach on enterprise and clinical benchmarks and compare it with prompt-based, gating, and supervised baselines. Experiments show that our method substantially reduces information leakage while preserving correctness and utility. These results suggest…

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  • arXiv cs.LGOct 20

    Unsupervised Head Network Personalization for Split Computing via Federated Learning

    Yutaro Horikawa, Takayuki Nishio

    Split Computing (SC) is a distributed inference method designed for balancing computational load and reducing latency by partitioning a neural network into head and tail networks and deploying them across both a mobile device and a server, respectively. Due to the limited bandwidth and computational power of mobile devices, the head network is often replaced with a neural network that requires lower computational and communication resources. However, it is challenging to prepare a sufficient quantity and quality of labeled data on the mobile device side for training the replacement head network. Furthermore, the data prepared by mobile users often contains sensitive privacy information, making data aggregation difficult. In this paper, we propose a data-efficient head network training method for SC. This method utilizes federated learning and knowledge distillation to train the head network using a small amount of non-IID and unlabeled data from mobile devices, ensuring that the training datasets are not exposed. Moreover, by introducing personalization, we enhance the accuracy to levels comparable to or exceeding those of existing methods. The experimental evaluations…

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  • arXiv cs.AIOct 20

    Privacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27B

    Mahendra Roopa, Srikanth Roopa

    Every prompt sent to a hosted language model is readable by whoever runs it. We study whether the prompt can remain on the client while the host receives transformed vectors that stand in for it. A language model converts text to vectors exactly once, with a public lookup table; we perform that step locally and pass the vectors through a small trained network (55.6M parameters) before they leave the perimeter. The host runs a frozen 27B model (Qwen3.8-27B) on the transformed vectors and never receives text or token ids. We train the transform against the frozen model as a fixed judge, combining a forward-KL utility term with an auxiliary term that pushes each vector away from its original direction. On 1,490 MMLU questions held out from training, the transform costs 1.5 points of accuracy (80.7% vs. 82.3%), breaking 68 of the 1,226 questions the unmodified model answers correctly (5.5%), while 30.1% of content tokens no longer decode to their original token under a nearest-neighbour lookup.

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  • arXiv cs.LGOct 20

    Privacy Accounting for Empirical Denoisers

    William Cheng, Mijung Park, Dongjun Kim

    Diffusion models have emerged as the foundation of modern generative systems, yet their high memorization capacity raises privacy concerns. While differentially private (DP) training provides formal guarantees, it comes at a cost in sample quality. In this work, we study a complementary question: how much privacy loss is incurred during the sampling process itself? Directly analyzing modern neural denoisers is challenging because their sensitivity to a single training example is generally intractable. To overcome this challenge, we construct and analyze a clipped privacy-accountable empirical denoiser, a training-free estimator of the Bayes-optimal denoiser, whose per-step sensitivity can be computed explicitly, allowing each denoising step to be a Gaussian mechanism. This perspective enables privacy accounting under Gaussian DP when each training example contributes to each denoising step, and under $f$-DP trade-off functions when each generated sample uses a single randomly subsampled block of the training data. We report every $(\varepsilon, δ)$ guarantee as a certified upper bound from a privacy loss distribution accountant, and use a cumulant-based Edgeworth approximation as…

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  • arXiv cs.CROct 20

    Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging

    Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea

    Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions, and enhances privacy by keeping raw data on local devices. Clients in FL systems train local models and share only updates, which are aggregated by a central server. While this setup limits direct data exposure, FL remains vulnerable to privacy attacks. Existing attack and defense strategies have largely been developed for centralized systems, and adapting them to FL introduces new challenges due to its unique architecture and training workflow. In this work, we first propose a model inversion attack on FL systems using Deep Convolutional Generative Adversarial Networks (DCGANs) in the context of large-scale medical imaging. Our method operates under realistic assumptions about the attacker's knowledge and capabilities. We then examine the effect of client-side differential privacy (DP) in mitigating privacy leakage and incorporate personalized FL to improve model performance under DP settings. Experiments on the Brain MRI ND-5 dataset show that DCGAN-based attacks can reconstruct semantically meaningful synthetic images, revealing…

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  • arXiv cs.LGOct 19

    LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning

    Saheed A. Tijani, Frank Jiang, Ming Liu

    Byzantine-robust aggregation shields federated learning from malicious updates, but the rules that filter out suspect updates do so by comparing clients' parameter vectors. That comparison requires access to every client's update, which secure aggregation exists to withhold, costs $O(n^2 d)$ in the parameter dimension $d$, and is defined only between clients that share an architecture. We propose LiBRA, which compares clients on what their models compute rather than on the parameters that compute them, using a fingerprint of $C^2$ values for $C$ classes, the class-conditional logit response of each client's model to pseudo-patterns optimised against it. We instantiate LiBRA as variants of Krum, Multi-Krum and Bulyan that change only what the dissimilarity score is computed on. On Fashion-MNIST and CIFAR-10 with three architectures and four backdoor and label-manipulation attacks under i.i.d. partitions, each variant holds every attack its parent holds, and on identical client updates under a backdoor attack the fingerprint ranks malicious clients within 0.006 AUC of the full parameter vector. The variants thus match the robustness of the rules they extend, while their trust…

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  • arXiv cs.CROct 19

    Guided Diffusion for Trustworthy Synthetic Text Generation

    Qi Pang, Hanshen Xiao, Andre Manoel, Lipika Ramaswamy, Wenting Zheng +2

    The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly output sensitive training data. Synthetic text generation offers a promising solution to this challenge; however, existing methods struggle to reconcile rigorous privacy guarantees with high data utility. In this paper, we propose a new paradigm for private synthetic text generation: Guided Diffusion. Leveraging the unique steerability of Diffusion Language Models, we introduce novel sampling and re-masking procedures that actively guide the iterative denoising steps. These mechanisms steer the generative process to reconstruct fluent text that adheres to a target semantic distribution. We demonstrate that our framework is naturally compatible with Differential Privacy (DP): once the semantic embeddings of the private corpus are protected, the DP guarantee carries over to the generated text by post-processing. Empirically, we show that our approach generates high-quality synthetic text with precise semantic alignment. Furthermore, we validate its effectiveness in DP synthetic text generation: across two…

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  • arXiv cs.CROct 19

    Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference

    Mingchen Li, Zifan Peng, Kewei Sha, Lavinia F Pieptea, Heng Fan +3

    Personalized large language models (LLMs) increasingly rely on profile-conditioned inference, pairing public requests with rich, user-specific facts. This paradigm exposes the user profile itself, together with the user's explicit sharing preferences. We introduce Profile-DP, a policy-aware local differential privacy framework designed specifically for user-profile release in personalized LLM inference. Profile-DP provably bounds the release of user-withheld facts while treating public instructions, profile schemas, and authorized facts as public side information. By generating a privatized profile that composes with the public instruction prior to answer generation, our framework decouples formal privacy guarantees from downstream model behavior. We develop two finite-domain mechanisms satisfying Profile-DP: DP-SLOT independently perturbs protected attributes, while DP-BLOCK uses public semantic decisions to coordinate their release and jointly sample related attributes. A positive, public compatibility prior connects these attributes while preserving a profile-level privacy bound. On PEEP, DP-BLOCK leads the utility composite among DP methods at all 13 structured budgets and is…

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  • arXiv cs.LGOct 19

    Learning to Refer: Inference-Time Collaboration in Decentralized Federated Learning

    Yuheng Ma, Chendi Wang, Qiong Zhang

    In decentralized federated learning, heterogeneous clients may develop complementary expertise, yet each query is typically evaluated only by the client where it arrives. To exploit this distributed expertise without global coordination, we formulate inference-time collaboration as budgeted local expert referral, a graph-constrained routing process in which a query can move only between neighboring clients and only visited clients contribute to its final prediction. EdgeDRM implements this framework by learning a density-based routing potential for each client: differences between neighboring potentials provide the comparisons used to select successive referrals, while local task models generate predictions that are aggregated along the resulting route. Both training and inference are decentralized, using only neighbor-local information without a central router or graph-wide client scoring. We establish estimation guarantees for the learned comparisons and characterize how local improvement and graph structure govern prior-weighted density gains from referral. Experiments on benchmark and real-world medical data show that EdgeDRM outperforms graph-matched decentralized baselines…

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  • arXiv cs.LGOct 19

    Communication-Efficient Federated Domain Generalization in Edge Networks via Dynamic Prototype Regulation

    Yuankun Xia, Hui Wang, Ling Yuan, Ziyu Hong, Di Yin +1

    Federated learning (FL) is a distributed paradigm for Internet of Things (IoT) edge networks. However, deploying Vision-Language Models (VLMs) over wireless edge networks presents distinct challenges: large model parameters increase uplink transmission latency over bandwidth-constrained fading channels, and non-independent and identically distributed (Non-IID) data exacerbates client drift. Existing feature alignment solutions often introduce auxiliary computations that consume time slots allocated for wireless parameter transmission, increasing the likelihood of straggler dropouts under system delay constraints. To address these issues, we propose DPGR-GCAM, a communication-efficient federated domain generalization framework. By employing a dual-parameter-efficient fine-tuning (PEFT) architecture, we design a channel-aware tiered transmission mechanism that adaptively truncates payloads to maximize node survival rates under fading conditions. To mitigate client drift, we introduce the Gradient-Conflict Aware Modulation (GCAM) mechanism. GCAM operates within the low-dimensional feature manifold to dynamically resolve multi-objective optimization conflicts with minimal edge…

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  • arXiv cs.ITOct 19

    Fundamental Limits of Information-Theoretic Secure Aggregation with User Dropouts in Federated Learning

    Jinbao Zhu, Kai Wan, Giuseppe Caire

    Secure aggregation enables a server to compute the sum of users' gradients or model updates without revealing individual values. With user dropouts, correlated random keys protect local gradients while allowing recovery of the desired gradient sum. We consider three phases: key distribution, masked-gradient upload, and mask elimination. Users establish correlated keys, upload masked gradients, and provide additional messages to eliminate residual aggregate masks. Existing information-theoretic schemes typically design mask-elimination communication for a prescribed dropout threshold and evaluate its cost under worst-case dropouts across the masked-gradient upload and mask-elimination phases. Such designs cannot adjust communication to actual dropout scenarios. Moreover, the randomness and communication costs of establishing correlated keys remain largely unexplored. To address these limitations, we formulate an adaptive secure aggregation framework under the T-colluding security constraint and construct a single scheme that supports all feasible dropout scenarios by adapting its communication to the actual number of dropouts. We establish converse bounds on four resources: key…

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  • arXiv cs.LGOct 18

    DROD: Dual-Ratio Ordered Dropout for Federated Continual Learning on Heterogeneous Devices

    Berk Sezer, Özgür Soysal

    Federated learning for capacity-heterogeneous clients and federated continual learning are each well studied on their own, but their intersection -- clients holding differently sized models while every client's data stream also drifts over time -- remains underexplored. We study this setting directly, training a ResNet-18 fleet from scratch on Split-CIFAR-100 (10 tasks, class-incremental), and introduce DROD (Dual-Ratio Ordered Dropout), which builds on FjORD's Ordered Dropout by using two independent reduction axes to extract sub-models: a shortcut ratio and a branch ratio. On a shared parameter/FLOP budget ladder, DROD achieves the highest mean accuracy among the heterogeneous-model federated learning strategies we compare. Its advantage over the two strongest baseline geometries persists across every replay-buffer size we test and under ER, DER++, and BiC; under EWC, all three geometries collapse alike.

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  • arXiv cs.LGOct 18

    Shallow Probes Are Sufficient for Near-Oracle Personalization Depth Allocation in Federated Learning

    Beyazit Bestami Yuksel, Emrah Dikbiyik, Ayse Yilmazer Metin

    Rather than proposing a new federated optimizer, we study whether a small client-specific measurement budget can support cost-aware personalization-depth decisions under a utility that trades Macro-F1 against deployment cost. Exhaustive evaluation of six depths defines an offline oracle that is not deployable. We audit the Probe Sufficiency Hypothesis: three shallow probes (P0--P2) may contain most decision-relevant information for approaching that oracle. FedPBA is a secondary learned instantiation for deep-action extrapolation. On CIFAR-10/100 and patient-disjoint PTB-XL (45 sessions; 900 clients; 2700 evaluations; GroupKFold), training-free greedy probing achieves regret 0.0071 and 98.3 percent oracle recovery, beating metadata-only policies and FedPBA (0.0110 / 97.3 percent). Aggregate shallow-best versus oracle correlation is 0.9818 but falls to 0.578 on deep-oracle rows; PTB-XL deep-oracle cases remain a high-regret boundary without a validated escalation gate.

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  • arXiv cs.LGOct 18

    Indistinguishable Agents: Training Agent Privacy as Twin-World Indistinguishability

    Jiaming He, Guanyu Hou, Yi Yu, Xudong Jiang

    LLM agents search, shop and send messages for their users, and every action is seen by some party. Existing privacy training rewards agents for not mentioning protected items, yet an observer can infer a private fact from what the agent does. Since an observer learns a fact only if its view changes with the fact, we propose \methodfull (\method), which trains on twin worlds that differ only in the private fact. A frozen distinguisher scores each observer's view by its log-likelihood ratio for the true world, and paired GRPO trains the agent on task success minus this ratio. We prove that this objective controls the Bayes success of every observer on every channel. On shopping and messaging tasks \method removes the leakage that disclosure judges and query-level rewards miss, while the agent still completes its tasks.

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  • arXiv cs.DSOct 18

    No Escape from the Quadratic Barrier: Adaptive Attacks on Linear $\ell_2$ Sketches

    Edith Cohen, Uri Stemmer

    Linear sketches estimate properties of high-dimensional inputs from a fixed low-dimensional projection. At constant accuracy, randomized Euclidean-norm sketches, notably the Johnson-Lindenstrauss transform, support exponentially many nonadaptive queries in sketch dimension $k$. For adaptive black-box queries, known privacy-based defenses guarantee $\widetildeΩ(k^2)$ and it remained open whether stronger guarantees were possible. Prior attacks used more queries, restricted the responder, or provided weaker failure guarantees. We show that the quadratic guarantee is optimal up to logarithmic factors, even with a computationally unbounded responder that knows the matrix, attack algorithm, history, and query distributions and may change its randomized rule after every query. For every fixed hidden $A\in\mathbb{R}^{k\times n}$ with $n>40k$ and target accuracy $α\in (0,1]$, our black-box attack uses $O(k+α^2k^2\log(2+α^2k))$ queries in $\widetilde{O}(1+α^2k)$ adaptive rounds to force, with constant probability, a round in which a constant fraction of answers have $Ω(α)$ relative error. Our results clarify the limits of defending a fixed compressive linear representation by adapting the…

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  • arXiv cs.CVOct 17

    PCHMR: Empowering and Benchmarking Human Mesh Recovery in Privacy-Constrained Real-World Settings

    Zeyu Cao, Qingxuan Wu, Zhiyang Dou, Rui Xu, Yuan Liu +4

    Fine-tuning directly on user-side data is an effective way to improve the performance of widely adopted data-driven human mesh recovery (HMR) models. However, doing so for existing HMR methods often assumes aggregating user-side data from real-world HMR-deployed devices to a central training server, posing a significant privacy risk as sensitive human images are transmitted. How can human mesh recovery be evaluated and improved in privacy-constrained real-world settings? We study this question through a benchmark in which state-of-the-art HMR models are trained with federated learning and a secure-aggregation variant, neither of which centralizes raw images, and evaluated across a wide range of client-scale and data-heterogeneity settings. We document that common HMR training pipelines are built around centralized data access and quantify how representative HMR backbones behave when that assumption is removed. Furthermore, to study the data bottleneck of privacy-constrained HMR training, we propose DePoser, a local annotation and fine-tuning pipeline enhanced with depth foundation models, with which collaboratively trained HMR models can be locally tailored to the end user's…

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  • arXiv cs.LGOct 17

    How Does Muon Work in Private Deep Learning? Scalable Differentially Private Training via Orthogonalization

    Alexander Gaponov, Grigory Malinovsky, Egor Shulgin, Peter Richtarik

    Training deep neural networks requires optimizers that balance computational efficiency with formal data privacy. While orthogonalized update rules, such as the recently introduced Muon optimizer, have shown strong empirical performance and scalability, their behavior under Differential Privacy (DP) is not yet understood. In this paper, we propose DP-Muon, a differentially private variant of the Muon optimizer. By analyzing Muon's orthogonalized updates through the lens of a Linear Minimization Oracle (LMO), we show that the leading effect of per-example clipping is a positive rescaling of the gradient, to which the LMO is blind, so that only a small directional residual controlled by the centered gradient variance enters the optimization error. This yields an $\mathcal O(K^{-1/4})$ convergence rate, up to logarithmic factors, to a clipping-dependent neighborhood at a fixed noise scale and, with noise calibrated to a fixed $(\varepsilon,δ)$ budget, the standard $(\sqrt d/(n\varepsilon))^{1/2}$ privacy--utility bound of DP-SGD, without assuming bounded gradients. Furthermore, we investigate the hyperparameter landscape of DP-Muon and document how the optimal learning rate,…

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Research · AI field

Federated learning

Found by the topic model: federated learning, differential privacy.

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50papers this week+38.9% vs last week189 this month

Open problems in Federated learning

  • Privacy-utility trade-off in federated learning4 papers raise it · 5 address it

    Balancing strong privacy guarantees with model performance remains a core challenge, especially for sensitive data like medical imaging or user profiles.

    • Privacy Accounting for Empirical Denoisersraises · addresses
    • Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imagingraises · addresses
    • Guided Diffusion for Trustworthy Synthetic Text Generationraises · addresses
    • Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inferenceraises · addresses
    • Privacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27Baddresses
  • Heterogeneous preferences and gradient conflicts in federated alignment2 papers raise it · 2 address it

    Diverse user preferences and competing objectives cause instability in collaborative training, degrading personalization and adaptation.

    • Collaborative Personalized Preference Alignment for LLMs under Data Deficiencyraises · addresses
    • Communication-Efficient Federated Domain Generalization in Edge Networks via Dynamic Prototype Regulationraises · addresses
  • Secure aggregation with user dropouts and communication constraints1 paper raises it · 1 addresses it

    Existing secure aggregation schemes struggle with dynamic dropouts and high communication costs, limiting scalability in real-world FL.

    • Fundamental Limits of Information-Theoretic Secure Aggregation with User Dropouts in Federated Learningraises · addresses
  • Byzantine-robust client selection without parameter access1 paper raises it · 1 addresses it

    Secure aggregation hides client updates, making it hard to detect malicious clients without exposing sensitive data.

    • LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learningraises · addresses
  • Decentralized inference-time collaboration in federated learning1 paper raises it · 1 addresses it

    Exploiting distributed expertise without central coordination is difficult, especially under graph constraints and limited budgets.

    • Learning to Refer: Inference-Time Collaboration in Decentralized Federated Learningraises · addresses

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