🤖 AI Summary
This paper addresses key challenges in integrating foundation models (FMs) with federated learning (FL)—namely, data silos, privacy compliance, computational constraints, model centralization, and difficulty in dynamic updates—by proposing a bidirectional FM-FL empowerment framework. Methodologically, it introduces three novelties: synthetic data augmentation, multimodal collaborative training, and decentralized FM co-construction, integrated with federated optimization, prompt-based fine-tuning, knowledge distillation, differential privacy, and generative data expansion. Contributions include: (i) a systematic analysis of mutual reinforcement mechanisms between FMs and FL; (ii) the first privacy-preserving, scalable, and sustainable FM-FL co-design principle and evaluation framework; and (iii) empirical validation demonstrating significant improvements in FL generalization and communication efficiency, while reducing FM training reliance on centralized data and compute resources—thereby providing theoretical foundations and practical pathways for privacy-aware, distributed large-model evolution.
📝 Abstract
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.
