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.
By Wenxuan Ye, Onur Ayan, Xueli An, Georg Carle
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. 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.
By Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen
arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.
By Guangzheng Hu, Patricia Men\'endez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng
arXiv:2608. 09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy.
By Radwan Selo, Majid Kundroo, Taehong Kim
Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. Wh...
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...
By Prashant Bajpai, Divya Saxena, Philippe Lalanda, German Vega
arXiv:2605. 11165v3 Announce Type: replace Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.
By Ben Rachmut, Luise Ge, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.
By Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee
arXiv:2607. 06616v1 Announce Type: cross Abstract: Label skew in federated learning (FL) causes client drift and degrades global accuracy.
By Sangwoo Lee, Sunghwan Park, Jaewoo Lee
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.
By Liam Mohr, Daphna Weinshall
The paper introduces CELM, a data‑free framework for federated learning that estimates class‑wise contribution by maximizing logits. It constructs a cross‑client evidence matrix to quantify each client’s competence and coverage for each class, then uses this matrix to compute weighted aggregation that upweights clients offering strong evidence for underrepresented classes. The method maintains stability through simplex constraints and momentum smoothing, and it is compatible with standard FL pipelines, showing improved robustness to class imbalance and heterogeneity on vision benchmarks.
By Asim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye, Karthik Nandakumar