TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency

TL;DR

TriplePlay leverages CLIP within federated learning to improve performance on non-IID data, reduce resource consumption, and enhance fairness across heterogeneous datasets.

Contribution

This work introduces TriplePlay, a novel framework that integrates CLIP with federated learning using adapters, quantization, and low-rank techniques for efficiency and fairness.

Findings

Reduces GPU resource usage and training time.

Achieves faster convergence with lower communication costs.

Enhances fairness in non-IID data scenarios.

Abstract

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.