March 13, 2024, 4:43 a.m. | Shangchao Su, Bin Li, Xiangyang Xue

cs.LG updates on

arXiv:2311.11227v2 Announce Type: replace
Abstract: With the increasing availability of Foundation Models, federated tuning has garnered attention in the field of federated learning, utilizing data and computation resources from multiple clients to collaboratively fine-tune foundation models. However, in real-world federated scenarios, there often exist a multitude of heterogeneous clients with varying computation and communication resources, rendering them incapable of supporting the entire model fine-tuning process. In response to this challenge, we propose a novel federated tuning algorithm, FedRA. The implementation …

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