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

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

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.05347

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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Computer Science

Federated Learning

Artificial Intelligence

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Computer science Resource (disambiguation) Federated learning Artificial intelligence Computer network

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.05347
PDF: https://arxiv.org/pdf/2409.05347
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4403617965

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DOI: https://doi.org/10.48550/arxiv.2409.05347

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

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-09-09

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

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Landing page: https://arxiv.org/abs/2409.05347

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PDF URL: https://arxiv.org/pdf/2409.05347

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Concepts: Computer science, Resource (disambiguation), Federated learning, Artificial intelligence, Computer network

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