arXiv:2607. 26618v1 Announce Type: new Abstract: Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples.
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.
Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples. However, task heterogeneity across clients can cause cross-task interference and gradient conflicts during aggregation.
The paper introduces FedTAR, a task-aware federated fine‑tuning approach for Mixture‑of‑Experts (MoE) large language models. FedTAR links local client updates to task preferences using routing outputs and Singular Value Decomposition to extract low‑dimensional task coordinates and update directions. It then aggregates updates within and across task clusters, reconstructing the final update to preserve expert specialization and reduce interference, achieving state‑of‑the‑art performance on four benchmark tasks under non‑IID settings.
arXiv:2604.23036v2 Announce Type: replace-cross Abstract: Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layer...
arXiv:2608. 10392v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts.
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.
arXiv:2606. 01007v1 Announce Type: cross Abstract: Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert communication and routing-induced load imbalance.