用CLIP提升联邦学习在非独立同分布数据下的效率与公平性
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
- 将CLIP作为适配器，增强联邦学习对异构数据的适应能力
- 通过量化与低秩适配降低资源消耗，通信开销减少且收敛更快
- 特别解决长尾分布问题，适合资源受限场景下的隐私保护应用
预训练模型（如CLIP）的快速发展为联邦学习（FL）带来了机遇与挑战，后者是隐私保护人工智能的关键。本文深入研究在联邦学习框架中集成大型基础模型（如CLIP）的复杂性，旨在提升隐私性、效率与跨异构数据环境的适应性。针对非独立同分布（non-IID）数据、模型计算与通信开销大、类别分布不均等问题，提出TriplePlay框架：利用CLIP作为适配器，增强对多样化数据分布的适应性，缓解长尾分布带来的不公平问题，并通过量化和低秩适配技术降低资源需求。仿真结果表明，TriplePlay显著降低GPU使用成本，加速学习过程，在减少通信开销的同时实现快速收敛。
原文摘要 · Abstract (English)
The rapid advancement and increasing complexity of pretrained models, exemplified by CLIP, offer significant opportunities as well as challenges for Federated Learning (FL), a critical component of privacy-preserving artificial intelligence. This research delves into the intricacies of integrating large foundation models like CLIP within FL frameworks to enhance privacy, efficiency, and adaptability across heterogeneous data landscapes. It specifically addresses the challenges posed by non-IID data distributions, the computational and communication overheads of leveraging such complex models, and the skewed representation of classes within datasets. We propose TriplePlay, a framework that integrates CLIP as an adapter to enhance FL's adaptability and performance across diverse data distributions. This approach addresses the long-tail distribution challenge to ensure fairness while reducing resource demands through quantization and low-rank adaptation techniques.Our simulation results demonstrate that TriplePlay effectively decreases GPU usage costs and speeds up the learning process, achieving convergence with reduced communication overhead.
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