Optimizing Multilingual LLMs via Federated Learning: A Study of Client Language Composition
Federated Learning (FL) of Large Language Models (LLMs) in multilingual environments presents significant challenges stemming from heterogeneous language distributions across clients and disparities in language resource availability. To address these challenges, we extended the FederatedScope-LLM framework to support multilingual instruction-tuning experiments with LLMs. We also introduced a novel client-specific early stopping mechanism, Local Dynamic Early Stopping (LDES-FL), which allows clients to pause and resume local training based on client-side validation performance, enhancing training efficiency and sustainability. Through a series of experiments, we studied how client language...
| Authors | Aleix Sant, Jordi Luque, Carlos Escolano |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 26 March 2026 |
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