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

Ahmed Imteaj, Md Zarif Hossain, Saika Zaman, Abdur R. Shahid

TL;DR

This paper proposes TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model's performance and adaptability across heterogeneous data distributions among the clients, and addresses the long-tail distribution problem in an FL environment.

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.

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

TL;DR

This paper proposes TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model's performance and adaptability across heterogeneous data distributions among the clients, and addresses the long-tail distribution problem in an FL environment.

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.
Paper Structure (25 sections, 9 equations, 7 figures, 1 algorithm)

This paper contains 25 sections, 9 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. Background Study
  4. Federated Learning
  5. Pretrained Vision-Language Models
  6. Proposed Approach
  7. Prioritizing Personalization and Generalization:
  8. Pretrained CLIP Model
  9. Adapter Architecture
  10. Adapter Integration
  11. Training Procedure
  12. Handling Long-Tail Distribution
  13. Discriminator's Goal
  14. Generator's Goal
  15. The Min-Max Game
  16. ...and 10 more sections

Figures (7)

  • Figure 1: (a) Overview of the TriplePlay system architecture, (b) FL client-sourced and GAN-generated image samples.
  • Figure 2: Image showcases the visual data from five different FL clients and the corresponding outputs generated by the TriplePlay.
  • Figure 3: GPU Utilization (left) and Test Accuracy (right) in Vanilla FedCLIP and our approach with on PACS Dataset.
  • Figure 4: Server accuracy comparison among FedCLIP, QLora fine-tuning without GAN, and TriplePlay on PACS dataset.
  • Figure 5: Server accuracy comparison among Vanilla FedCLIP, FedCLIP with QLora, and TriplePlay on Office-Home dataset.
  • ...and 2 more figures