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When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

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arxiv 2306.15546 v3 pith:GHLH2HBG submitted 2023-06-27 cs.LG cs.AIcs.DC

Weiming Zhuang , Chen Chen , Jingtao Li , Chaochao Chen , Yaochu Jin , Lingjuan Lyu This is my paper
classification cs.LGcs.AIcs.DC
keywords datachallengesfoundationfuturelearningcollaborativedevelopmentdirections
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The intersection of Foundation Model (FM) and Federated Learning (FL) presents a unique opportunity to unlock new possibilities for real-world applications. On the one hand, FL, as a collaborative learning paradigm, help address challenges in FM development by expanding data availability, enabling computation sharing, facilitating the collaborative development of FMs, tackling continuous data update, avoiding FM monopoly, response delay and FM service down. On the other hand, FM, equipped with pre-trained knowledge and exceptional performance, can serve as a robust starting point for FL. It can also generate synthetic data to enrich data diversity and enhance overall performance of FL. Meanwhile, FM unlocks new sharing paradigm and multi-task and multi-modality capabilities for FL. By examining the interplay between FL and FM, this paper presents the motivations, challenges, and future directions of empowering FL with FM and empowering FM with FL. We hope that this work provides a good foundation to inspire future research efforts to drive advancements in both fields.

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Cited by 10 Pith papers

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  4. Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation

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    Fed-HeLLo allocates different LoRA layers to clients of different resource levels using importance scores and geometric patterns, improving federated fine-tuning accuracy over random allocation baselines.

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