Similarity-Aware Personalized Federated Learning in Heterogeneous Environments
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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.
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: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.
The paper introduces FedRoRA, a federated learning framework that combines Low‑Rank Adaptation (LoRA) with rank‑heterogeneous personalization. It separates model adaptation into shared global directions and client‑specific rank‑wise magnitudes, using SVD on the server to extract a global subspace and a personalized projection with top‑k selection for each client. Experiments on natural language understanding and generation tasks show that FedRoRA outperforms existing state‑of‑the‑art methods.
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
arXiv:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.