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New FedSEAM framework enables continual learning for LLMs across clients

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FedSEAM, a new framework for federated continual learning of large language models. This approach allows models to learn from multiple clients without sharing data, while also retaining knowledge from previous tasks. FedSEAM organizes task knowledge into sparse experts and uses a subspace-based compatibility test to determine which experts to reuse and where to introduce new capacity. The framework also employs Subspace Expert Agreement Merging (SEAM) on the server side to guide clients towards consensus without disrupting task-specific specializations, addressing issues like routing drift and merge erasure. AI

IMPACT This research could enable more efficient and private training of large language models across distributed datasets.

RANK_REASON The cluster contains a research paper detailing a new framework for federated continual learning of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New FedSEAM framework enables continual learning for LLMs across clients

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The cluster contains a research paper detailing a new framework for federated continual learning of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Fatema Siddika, Abhijit Chunduru, Zilinghan Li, Ali Jannesari, Ravi Madduri ·

    FedSEAM: Federated Continual Learning of LLMs via Subspace Expert Agreement Merging

    arXiv:2610.33496v1 Announce Type: new Abstract: Federated continual learning trains a model on a sequence of tasks across several clients without sharing their data while retaining knowledge of earlier tasks. Sparse mixture-of-experts models support this setting by adapting selec…

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