Gradient clipping eliminates the dependence on maximum delay in the oracle complexity of asynchronous SGD, yielding convergence in expectation and high probability under sub-Weibull gradient noise.
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Adaptive personalized federated learning
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UNVERDICTED 19representative citing papers
A federated actor-critic framework lets agents share a linear subspace representation for policies while maintaining personalized local actors and critics, achieving critic error and policy gradient convergence rates of order 1 over square root of TK with linear speedup in K agents under environment
FedOBP introduces a quantile-thresholded importance score based on a federated first-order Taylor approximation to select a small set of parameters for personalization, claiming better performance than prior PFL methods.
A two-layer privacy system using skeletal abstraction and federated learning enables multi-site training for child autism behavior recognition and outperforms standard federated baselines on the MMASD benchmark.
Federated Q-learning in heterogeneous environments achieves linear speedup in K agents for sampling error but is limited to Θ(E/T) convergence when averaging every E steps, with a two-phase error decay-then-rise behavior in experiments.
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
Introduces range penalization for federated linear models that identifies shared weights and performs polar clustering on personalized features, supported by new nonasymptotic proofs and a fast optimization algorithm.
FedSAP stabilizes federated prototype learning via a deterministic alignment curriculum and proxy separation loss, reporting up to 4 percentage point gains under high heterogeneity across three benchmarks.
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
A taxonomy of client-level disagreements in federated learning is presented together with a multi-track resolution strategy that enforces strict exclusion via isolated update paths, shown to handle permanent, temporal, and overlapping patterns in simulations on MNIST and N-CMAPSS.
FMTC learns personalized clustering models on clients and uses server-side tensor low-rank regularization to capture shared structure across heterogeneous clients in a privacy-preserving federated setting.
COSMOS personalizes federated learning across heterogeneous client models via server-side clustering and distillation using only pseudo-labels, with claimed exponential personalization risk contraction.
PGFedSplit improves client-specific performance and global generalization in heterogeneous federated learning via split architectures, adaptive aggregation, and local-plus-synthetic representation mixing.
FIRMA introduces Fibonacci ring aggregation protocols for server-free federated learning that maintain private heads and achieve higher accuracy than FedAvg under label skew across multiple benchmarks and heterogeneity regimes.
Fed-BAC uses contextual bandits and Thompson Sampling with additive clustering to deliver up to 35.5 percentage point accuracy gains and 1.5-4.8x faster convergence in hierarchical federated learning on non-IID data.
Fine-tuning impairs the class balance of foundation models in long-tailed personalized federated learning, which FedPuReL addresses through gradient purification using zero-shot predictions and residual-based personalization to achieve better global and local performance.
FedRio is a new federated framework that outperforms standard federated baselines in social bot detection accuracy and efficiency while staying competitive with centralized models under stronger privacy constraints.
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.
citing papers explorer
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Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
Gradient clipping eliminates the dependence on maximum delay in the oracle complexity of asynchronous SGD, yielding convergence in expectation and high probability under sub-Weibull gradient noise.
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Collaborative Yet Personalized Policy Training: Single-Timescale Federated Actor-Critic
A federated actor-critic framework lets agents share a linear subspace representation for policies while maintaining personalized local actors and critics, achieving critic error and policy gradient convergence rates of order 1 over square root of TK with linear speedup in K agents under environment
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FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
FedOBP introduces a quantile-thresholded importance score based on a federated first-order Taylor approximation to select a small set of parameters for personalization, claiming better performance than prior PFL methods.
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Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis
A two-layer privacy system using skeletal abstraction and federated learning enables multi-site training for child autism behavior recognition and outperforms standard federated baselines on the MMASD benchmark.
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On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Federated Q-learning in heterogeneous environments achieves linear speedup in K agents for sampling error but is limited to Θ(E/T) convergence when averaging every E steps, with a two-phase error decay-then-rise behavior in experiments.
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Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
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Range Penalization: Theoretical Insights with Applications in Federated Learning
Introduces range penalization for federated linear models that identifies shared weights and performs polar clustering on personalized features, supported by new nonasymptotic proofs and a fast optimization algorithm.
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Closing the Alignment-Maturity Gap in Federated Prototype Learning
FedSAP stabilizes federated prototype learning via a deterministic alignment curriculum and proxy separation loss, reporting up to 4 percentage point gains under high heterogeneity across three benchmarks.
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Personalized Digital Health Modeling with Adaptive Support Users
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
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A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
A taxonomy of client-level disagreements in federated learning is presented together with a multi-track resolution strategy that enforces strict exclusion via isolated update paths, shown to handle permanent, temporal, and overlapping patterns in simulations on MNIST and N-CMAPSS.
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Federated Multi-Task Clustering
FMTC learns personalized clustering models on clients and uses server-side tensor low-rank regularization to capture shared structure across heterogeneous clients in a privacy-preserving federated setting.
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COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
COSMOS personalizes federated learning across heterogeneous client models via server-side clustering and distillation using only pseudo-labels, with claimed exponential personalization risk contraction.
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Separate Aggregation of Split Network for Personalized Federated Learning
PGFedSplit improves client-specific performance and global generalization in heterogeneous federated learning via split architectures, adaptive aggregation, and local-plus-synthetic representation mixing.
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FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning
FIRMA introduces Fibonacci ring aggregation protocols for server-free federated learning that maintain private heads and achieve higher accuracy than FedAvg under label skew across multiple benchmarks and heterogeneity regimes.
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Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
Fed-BAC uses contextual bandits and Thompson Sampling with additive clustering to deliver up to 35.5 percentage point accuracy gains and 1.5-4.8x faster convergence in hierarchical federated learning on non-IID data.
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Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Fine-tuning impairs the class balance of foundation models in long-tailed personalized federated learning, which FedPuReL addresses through gradient purification using zero-shot predictions and residual-based personalization to achieve better global and local performance.
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FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation
FedRio is a new federated framework that outperforms standard federated baselines in social bot detection accuracy and efficiency while staying competitive with centralized models under stronger privacy constraints.
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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
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Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.