arXiv:2609.07192v1 Announce Type: cross Abstract: Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather...
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.
Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillat...
FedCC is a new algorithm for distillation-based federated learning that tackles label distribution skew by allowing clients to mark ambiguous samples as 'unknown' instead of forcing a potentially wrong classification. By adding this extra class and calibrating pseudo-labels on a public dataset, FedCC balances confidence across majority and minority classes. Experiments show that FedCC outperforms existing methods, achieving 67.3% accuracy even when each client has data from only one of ten classes, whereas baselines drop to near-random performance.
arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.
arXiv:2608. 02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models.
\textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients. However, under client heterogeneity, the conventional static split stra...
arXiv:2610.20264v1 Announce Type: new Abstract: Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minim...
The paper introduces a federated active learning (FAL) approach that tackles data privacy and label scarcity by coordinating query selection across clients. In low-budget scenarios, it finds that homogeneous (IID) data actually requires stronger coordination to avoid redundant queries, while heterogeneous data naturally yields diversity—a reversal of the usual federated learning narrative. The authors propose a new framework that aligns client data in a shared embedding space via federated representation learning, enabling globally coordinated active selection while keeping annotations local, and demonstrate that this method outperforms existing FAL methods even with larger annotation budgets.
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.
arXiv:2508. 05157v2 Announce Type: replace Abstract: Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits.