arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.
arXiv:2608. 15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously.
arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).
The paper investigates federated learning where each client’s data consists of unknown mixtures of distinct tasks, a scenario termed compound heterogeneity. It shows that when tasks share a common feature geometry, the optimal model for a mixed client is a convex combination of task‑specific models, motivating input‑dependent routing to specialized experts. The authors propose FedSEE, a method that recovers task experts via a convex program and achieves better performance than baselines, reducing negative transfer by 2.9 points overall and 3.7 points for the worst‑served quartile.