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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

Named by an AI from these papers' abstracts.

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differential privacy · private learning · differentially private99 papers28 this week
federated learning · federated learning fl · distributed learning102 papers23 this week

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arXiv cs.LGOct 1
FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization
Hang Zou, Chao Zhang, Yuzhi Yang, Yu Tian, Samson Lasaulce +1
Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, applying Low-Rank Adaptation (LoRA) in FL faces a fundamental "aggregation dilemma" between the accurate Sum-of-Products (SoP) and the communication-efficient Product-of-Sums (PoS) implementations. To tackle these challenges, we propose FedFit. First, to significantly reduce communication overhead, we introduce a disjoint shared vector-bank parameterization that reconstructs high-dimensional adapter matrices from two compact and disjoint global vector banks. Second, to address the aggregation dilemma, we devise an alternating optimization schedule. By cycling between decoupled single-bank updates (which allow for accurate aggregation) and joint updates corrected by a Residual Spectral Aggregation mechanism, we resolve the conflict between SoP and PoS. Additionally, we integrate blockwise quantization with client-side error feedback to further compress the transmitted vectors. Furthermore, we establish theoretical convergence guarantees for the proposed algorithm. Extensive experiments on Qwen2.5 models…
PDF arXiv
arXiv cs.NIOct 1
Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport
Emilio Paolini, Andrea Pinto, Flavio Esposito, Luca Valcarenghi
Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This mismatch is particularly problematic for optical circuit switching and all-photonics transport, which benefit from predictable and schedulable traffic demands. We argue that future RANs should act as learning-aware traffic shapers by exposing the communication structure of distributed model adaptation to the transport layer. Through in-network aggregation at the gNB, asynchronous UE updates can be transformed into fewer aggregate transfers with bounded size and delivery requirements. Once shaped…
PDF arXiv
arXiv cs.AIOct 1
Federated Agent Optimization
Qiang Yang, Zhiqiang Kou, Xueyi Zhang, Dong-Dong Wu, Hanlin Gu +4
Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowledge. Yet such experience is distributed across organizations and cannot be directly shared because of privacy and proprietary constraints. Conventional federated learning is insufficient for this setting, as agent capabilities extend beyond model parameters to memory, tools, rewards, skills, and structured knowledge. In this paper, we formulate \textbf{Federated Agent Optimization (FAO)}, which studies how distributed agents can collaboratively improve through controlled information exchange while keeping raw data, complete trajectories, and private knowledge local. We define FAO as a multi-objective problem balancing agent utility, privacy leakage, and communication cost, and organize its optimization space across policy, memory, tool use, reward, and structured knowledge and skills. We further characterize how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities, providing a unified view of how agents can benefit from one another without direct experience…
PDF arXiv
arXiv cs.LGOct 1
Latent Information Sharing for Accelerating Federated Learning
Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee
Federated learning (FL) is a communication-efficient distributed learning paradigm. However, client drift remains one of the most critical challenges, hindering the efficient training of a global model. In this study, we propose a novel latent information sharing scheme that directly mitigates data heterogeneity across clients. Our theoretical and empirical results show that sharing a small amount of hidden-layer activations significantly improves training efficiency while preserving convergence guarantees and data privacy. Furthermore, we compare our method with existing FL approaches designed to address client drift, including FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, and demonstrate superior model accuracy under a fixed round budget without incurring excessive communication overhead. Overall, this work presents a promising new knowledge aggregation scheme and provides a comprehensive analysis of the impact of activation sharing on federated optimization.
PDF arXiv
arXiv cs.CRSep 30
Progressive-Resolution Secure Aggregation for Federated Learning
Seyed Mohammad Azimi-Abarghouyi
Secure aggregation lets a server recover an aggregate of client updates without observing any individual update, but conventional protocols fix the aggregate precision when clients upload. We introduce and formulate a new progressive-resolution secure-aggregation functionality in which clients upload once and successively finer resolutions of the same aggregate can later be authorized without renewed client participation. To realize this functionality, we propose progressive-resolution secure aggregation (PSA): each clipped, dithered update is represented by compatible nested-lattice digits; separately releasable layers are protected by secure aggregation and an additional aggregate pad that remains unavailable to the server until a non-colluding release controller authorizes that layer.
PDF arXiv
arXiv cs.CVSep 30
FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification
Barış Büyüktaş, Begüm Demir
Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentralized RS image archives without requiring direct access to local data. However, FL performance significantly degrades when the data distributions between clients are heterogeneous, which often occurs due to geographical differences, seasonal changes, and varying image acquisition and atmospheric conditions. To address this challenge, in this letter, we propose a novel personalized FL framework (denoted as FedMAD) for RS image classification problems. The proposed framework separates globally shared representation parameters from client-specific adaptation parameters to preserve client-specific features while maintaining globally transferable representations. This is achieved by integrating lightweight modulation modules and local batch normalization layers into the backbone network. Although globally shared parameters are collaboratively optimized between clients, client-specific parameters remain local to preserve domain-specific feature characteristics. In addition, FedMAD introduces a modulation-aware directional aggregation…
PDF arXiv

Papers

Topics in this field

differential privacy · private learning · differentially private99 papers28 this week
federated learning · federated learning fl · distributed learning102 papers23 this week

Recent papers

All in Research →

arXiv cs.LGOct 1
FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization
Hang Zou, Chao Zhang, Yuzhi Yang, Yu Tian, Samson Lasaulce +1
Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, applying Low-Rank Adaptation (LoRA) in FL faces a fundamental "aggregation dilemma" between the accurate Sum-of-Products (SoP) and the communication-efficient Product-of-Sums (PoS) implementations. To tackle these challenges, we propose FedFit. First, to significantly reduce communication overhead, we introduce a disjoint shared vector-bank parameterization that reconstructs high-dimensional adapter matrices from two compact and disjoint global vector banks. Second, to address the aggregation dilemma, we devise an alternating optimization schedule. By cycling between decoupled single-bank updates (which allow for accurate aggregation) and joint updates corrected by a Residual Spectral Aggregation mechanism, we resolve the conflict between SoP and PoS. Additionally, we integrate blockwise quantization with client-side error feedback to further compress the transmitted vectors. Furthermore, we establish theoretical convergence guarantees for the proposed algorithm. Extensive experiments on Qwen2.5 models…
PDF arXiv
arXiv cs.NIOct 1
Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport
Emilio Paolini, Andrea Pinto, Flavio Esposito, Luca Valcarenghi
Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This mismatch is particularly problematic for optical circuit switching and all-photonics transport, which benefit from predictable and schedulable traffic demands. We argue that future RANs should act as learning-aware traffic shapers by exposing the communication structure of distributed model adaptation to the transport layer. Through in-network aggregation at the gNB, asynchronous UE updates can be transformed into fewer aggregate transfers with bounded size and delivery requirements. Once shaped…
PDF arXiv
arXiv cs.AIOct 1
Federated Agent Optimization
Qiang Yang, Zhiqiang Kou, Xueyi Zhang, Dong-Dong Wu, Hanlin Gu +4
Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowledge. Yet such experience is distributed across organizations and cannot be directly shared because of privacy and proprietary constraints. Conventional federated learning is insufficient for this setting, as agent capabilities extend beyond model parameters to memory, tools, rewards, skills, and structured knowledge. In this paper, we formulate \textbf{Federated Agent Optimization (FAO)}, which studies how distributed agents can collaboratively improve through controlled information exchange while keeping raw data, complete trajectories, and private knowledge local. We define FAO as a multi-objective problem balancing agent utility, privacy leakage, and communication cost, and organize its optimization space across policy, memory, tool use, reward, and structured knowledge and skills. We further characterize how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities, providing a unified view of how agents can benefit from one another without direct experience…
PDF arXiv
arXiv cs.LGOct 1
Latent Information Sharing for Accelerating Federated Learning
Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee
Federated learning (FL) is a communication-efficient distributed learning paradigm. However, client drift remains one of the most critical challenges, hindering the efficient training of a global model. In this study, we propose a novel latent information sharing scheme that directly mitigates data heterogeneity across clients. Our theoretical and empirical results show that sharing a small amount of hidden-layer activations significantly improves training efficiency while preserving convergence guarantees and data privacy. Furthermore, we compare our method with existing FL approaches designed to address client drift, including FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, and demonstrate superior model accuracy under a fixed round budget without incurring excessive communication overhead. Overall, this work presents a promising new knowledge aggregation scheme and provides a comprehensive analysis of the impact of activation sharing on federated optimization.
PDF arXiv
arXiv cs.CRSep 30
Progressive-Resolution Secure Aggregation for Federated Learning
Seyed Mohammad Azimi-Abarghouyi
Secure aggregation lets a server recover an aggregate of client updates without observing any individual update, but conventional protocols fix the aggregate precision when clients upload. We introduce and formulate a new progressive-resolution secure-aggregation functionality in which clients upload once and successively finer resolutions of the same aggregate can later be authorized without renewed client participation. To realize this functionality, we propose progressive-resolution secure aggregation (PSA): each clipped, dithered update is represented by compatible nested-lattice digits; separately releasable layers are protected by secure aggregation and an additional aggregate pad that remains unavailable to the server until a non-colluding release controller authorizes that layer.
PDF arXiv
arXiv cs.CVSep 30
FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification
Barış Büyüktaş, Begüm Demir
Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentralized RS image archives without requiring direct access to local data. However, FL performance significantly degrades when the data distributions between clients are heterogeneous, which often occurs due to geographical differences, seasonal changes, and varying image acquisition and atmospheric conditions. To address this challenge, in this letter, we propose a novel personalized FL framework (denoted as FedMAD) for RS image classification problems. The proposed framework separates globally shared representation parameters from client-specific adaptation parameters to preserve client-specific features while maintaining globally transferable representations. This is achieved by integrating lightweight modulation modules and local batch normalization layers into the backbone network. Although globally shared parameters are collaboratively optimized between clients, client-specific parameters remain local to preserve domain-specific feature characteristics. In addition, FedMAD introduces a modulation-aware directional aggregation…
PDF arXiv

Active contributors · 90 days

Takayuki Nishio

Unsupervised Head Network Personalization for Split Computing via Federated Learning

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Activity · 12 weeks

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Fields · AI · updated 53m ago

Federated learning

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

51papers this week+37.8% vs last week

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192 papers 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

Named by an AI from these papers' abstracts.