federated-learning

FedImp is a novel federated learning algorithm that uses impurity-based weighting to enhance convergence speed in non-IID data settings, showing significant reductions in communication rounds compared to baselines.

This paper proposes a cross-layer polar code based federated learning scheme to address communication bottlenecks and channel impairments, providing convergence analysis and resource optimization that demonstrates performance gains over uncoded and LDPC-based benchmarks.

This paper introduces a hybrid quantum-inspired Kolmogorov-Arnold network for privacy-aware federated learning of ECG data, demonstrating reduced parameters and communication costs while improving classification metrics compared to traditional MLP.

MedMix is a semantic-alignment framework for federated multimodal sparse Mixture-of-Experts that addresses modality heterogeneity by coordinating routing and expert specialization, achieving improved performance in medical AI datasets.

This paper presents a comprehensive survey of federated prompt learning (FPL), reviewing advances in integrating federated learning with large language models, discussing motivations, trade-offs, and future research directions.

This paper proposes FedCoMuon, a federated compositional Muon optimizer for matrix-wise models, along with a variance-reduced variant (FedCoMuon-VR). The authors provide convergence analysis under non-i.i.d. and non-convex settings, showing improved sample complexity over existing FedMuon algorithms, and demonstrate competitive performance on robust federated learning and task-distributed risk-sensitive meta learning.

This paper proposes Hierarchical Federated Transfer Learning (HFTL) for Digital Twin-based Vehicular Ad hoc Networks, addressing data heterogeneity and sparsity via vehicle clustering and a data quality score mechanism to defend against malicious vehicles.

This paper presents a reconstructed benchmark analyzing federated aggregation methods under model poisoning and backdoor attacks, finding Trimmed Mean most accurate in clean settings and Krum most robust under attacks, while auditing metric implementations and noting reproducibility caveats.

This paper investigates federated learning for CNC tool wear prediction, showing that federated models achieve performance close to centralized learning and surpass local client baselines in distributed manufacturing environments.

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.