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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Adaptive personalized federated learning
Canonical reference. 80% of citing Pith papers cite this work as background.
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UNVERDICTED 17representative citing papers
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.
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.
COSMOS clusters clients via pseudo-label predictions on public data, trains cluster-specific server models, and distills them to clients, claiming exponential personalization risk contraction and outperforming model-agnostic FL baselines.
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.
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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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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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.