Scholarlink

<p>The rapid advancement of Artificial Intelligence introduces significant challenges related to computational efficiency, data privacy, and distributed data management across diverse environments. Federated Learning (FL) effectively addresses these challenges by enabling decentralized training while simultaneously preserving data privacy, but it often struggles with effective personalization, especially in non-IID (non-Independent and Identically Distributed) data scenarios commonly found in real-world applications. To tackle this issue, we propose Green-EDP, a novel and modular FL architecture that balances global generalization and local adaptation by leveraging an Encoder-Decoder-based architecture. The encoder, hosted on the central server, aggregates shared knowledge from all participating clients, while the decoder, private to each individual client, integrates these global insights with specific local data to enhance personalized model performance. Our method is fully modular and can be flexibly combined with different FL aggregation techniques, optimizers, and various personalization strategies. We evaluate Green-EDP on multiple federated datasets (EMNIST, CelebA, CIFAR-10) and demonstrate that it achieves superior accuracy and significantly faster convergence while maintaining per-round training times comparable to baselines. At the same time, by transmitting only the encoder parameters, Green-EDP reduces the communication cost per round by an order of magnitude, which together with its quicker convergence lowers the overall computational and communication footprint. This dual efficiency aligns Green-EDP with Green AI principles, offering a sustainable and effective solution for personalized federated learning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Green-EDP: aligning personalization in federated learning and green artificial intelligence throughout the encoder-decoder architecture

  • 期刊论文

  • 发表日期：

    2025年12月11日

查看全文
  • José Antonio Ruiz-Millán,
  • Nuria Rodríguez-Barroso,
  • M. Victoria Luzón

摘要

The rapid advancement of Artificial Intelligence introduces significant challenges related to computational efficiency, data privacy, and distributed data management across diverse environments. Federated Learning (FL) effectively addresses these challenges by enabling decentralized training while simultaneously preserving data privacy, but it often struggles with effective personalization, especially in non-IID (non-Independent and Identically Distributed) data scenarios commonly found in real-world applications. To tackle this issue, we propose Green-EDP, a novel and modular FL architecture that balances global generalization and local adaptation by leveraging an Encoder-Decoder-based architecture. The encoder, hosted on the central server, aggregates shared knowledge from all participating clients, while the decoder, private to each individual client, integrates these global insights with specific local data to enhance personalized model performance. Our method is fully modular and can be flexibly combined with different FL aggregation techniques, optimizers, and various personalization strategies. We evaluate Green-EDP on multiple federated datasets (EMNIST, CelebA, CIFAR-10) and demonstrate that it achieves superior accuracy and significantly faster convergence while maintaining per-round training times comparable to baselines. At the same time, by transmitting only the encoder parameters, Green-EDP reduces the communication cost per round by an order of magnitude, which together with its quicker convergence lowers the overall computational and communication footprint. This dual efficiency aligns Green-EDP with Green AI principles, offering a sustainable and effective solution for personalized federated learning.