federated-learning

This paper introduces EDPFRL-IM, a framework that integrates curiosity-driven intrinsic motivation into personalized federated reinforcement learning to improve exploration in sparse-reward, non-stationary environments while preserving client privacy.

SeFoRA is a proposed federated LoRA algorithm that uses sketch aggregation to handle heterogeneous client ranks and alleviate bilinear mismatch. It includes a rank-homogeneous variant with convergence guarantees and shows state-of-the-art performance on RoBERTa-Large fine-tuning.

This paper introduces Sheaf-based Federated Representation Learning (SFRL), a framework that aligns heterogeneous local representations via learnable sheaf restriction maps and a quadratic gluing regularizer, without assuming a shared global latent space. A decentralized algorithm (Sheaf-FRL) with convergence guarantees is proposed and shown to outperform baselines in cooperative classification under data and model heterogeneity.

This paper introduces Krum-Proxy, a selection-aware backdoor attack that bypasses distance-based robust aggregation methods like Krum in federated learning by optimizing adversarial updates to mimic benign geometry, achieving high attack success while preserving clean accuracy.

This thesis tackles seven challenges in distributed and federated optimization, introducing methods like ProxSkip and Variance Reduced ProxSkip, and establishing theoretical foundations for communication-efficient, robust, and practical algorithms.

This paper proposes FedLBW, a federated learning aggregation strategy that weights client updates by inverse validation loss instead of dataset size, improving accuracy and robustness to non-IID data and client dropouts in wireless networks.

A tweet from The Turing Post recommending Alexander Jung's book 'Machine Learning: The Basics' as a compact refresher covering the data-model-loss framework, including hypothesis spaces, model selection, ERM, regularization, probabilistic models, clustering, federated learning, privacy, and explainability.

This arXiv paper proposes collaborative control algorithms for federated multi-agent systems with AI agents and critics, applied to fault detection and cause analysis in network telemetry, with convergence guarantees via multi-time scale stochastic approximation.

Presents DG-FedReuse, a federated learning mechanism that reuses age-decayed cached client updates under a proxy-gradient threshold to reduce uplink communication, achieving significant modeled savings with minimal accuracy loss on image classification benchmarks.

This paper presents a controlled study of federated learning for aircraft-engine remaining-useful-life prediction under both benign and adversarial client heterogeneity, evaluating personalization and Byzantine-robust aggregation methods. It finds that shared-representation personalization closes much of the local-central accuracy gap, robust aggregation with Krum effectively mitigates backdoor attacks, and combining both yields a composed defense with low attack success at a modest accuracy cost.

This arXiv paper evaluates federated training of tokenized generative event models (GEMs) on ICU EHR data from three health systems, showing that federated learning preserves most centralized performance and improves cross-site transportability compared to conventional supervised models.

Proposes FedTCR, the first systematic federated multimodal graph learning algorithm that handles task, modality, and topology heterogeneity via topology-aware cross-modal routing and tri-level contrastive learning, outperforming baselines across 7 domains.

This paper proposes FedSLM, a parameter-centric framework for federated fine-tuning of foundation models with heterogeneous compressed clients, using SVD-based decomposition and a weak-to-strong elicitation step to handle resource asymmetry. Experiments show it outperforms existing federated baselines while reducing client GPU memory by ~50%.

This paper investigates evaluation protocols for federated pre-trained models, showing that downstream fine-tuning does not reliably preserve pre-training quality rankings, while direct next-token prediction strongly aligns with pre-training perplexity.

The paper proposes MMFGU, a multimodal federated graph unlearning framework that decouples target-specific representations to handle entity, modality, and pairing removal requests while preserving retained utility, achieving a 41.5x speedup over full retraining.

This paper proposes F2CTO, the first distributed first-order constrained trilevel optimization method for robust coreset selection over distributed networks, with a non-asymptotic convergence guarantee of O(ε^(-3/2)).

FedWeave proposes asymmetric aggregation for federated MoE-LoRA to handle task heterogeneity by separating expert aggregation from router optimization, achieving better specialization and performance.

This paper presents the first systematic study of federated training for SpeechLLM-based end-to-end ASR systems, evaluating on English and Italian tasks and achieving competitive word error rates with reduced communication costs.

DP-FedSOFIM moves curvature estimation to the server in differentially private federated learning, achieving the same privacy guarantee as DP-FedGD with O(d) client memory and significant early-round accuracy gains.

This paper introduces a mean-field privacy game framework for federated learning, enabling tractable Nash equilibrium analysis for arbitrarily many clients with heterogeneous privacy preferences and yielding a personalized privacy guarantee.