FedSEAM: Federated Continual Learning of LLMs via Subspace Expert Agreement Merging
Read the original on arXiv Machine Learning →FedSEAM is a federated continual learning framework that trains large language models across multiple clients without sharing data, using sparse mixture-of-experts to adapt selected parameters on a frozen backbone. It introduces task-aware tile selection for new tasks and a subspace-based compatibility test to decide which expert tiles to reuse or replace. On the server, Subspace Expert Agreement Merging (SEAM) aligns client updates only along agreed subspaces, while additional protections mitigate routing drift and merge erasure, preserving prior knowledge during training.
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.