FedHUG introduces a Federated Unsupervised Domain Generalization protocol to address privacy and labeling challenges in remote physiological measurement. The framework includes a Minimal Bias Aggregation module that adapts weights to heterogeneous non‑IID features and a Global Distribution‑aware Learning Controller that adjusts client training to mitigate label skew and long‑tail issues. Experiments demonstrate superior performance over state‑of‑the‑art methods using RGB video or mmWave radar data.
Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence.
arXiv:2608. 09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy.
arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.
arXiv:2610.38970v2 Announce Type: replace Abstract: Current and emerging mobile networks face increasing privacy, scalability, and performance challenges as distributed learning becomes essential for...
arXiv:2610.00693v1 Announce Type: new Abstract: Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentr...
arXiv:2608. 15639v1 Announce Type: cross Abstract: \textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients.
arXiv:2607. 06653v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data.
arXiv:2608. 13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training.
The paper introduces pFedMARL, a federated learning framework that uses multi‑agent reinforcement learning with TD3 to dynamically adjust client contributions and personalize models. It applies a server‑side agent to optimize global aggregation and client‑side agents to balance global and local updates, eliminating the need for pre‑training. Experiments on a semi‑supervised audio spectrogram transformer show that pFedMARL outperforms or matches FedAvg, Ditto, and local training across various non‑IID settings and against adversarial clients, improving accuracy, robustness, and fairness.
FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.