Summary of Towards Energy-aware Federated Learning Via Marl: a Dual-selection Approach For Model and Client, by Jun Xia and Yi Zhang and Yiyu Shi
Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
by Jun Xia, Yi Zhang, Yiyu Shi
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an energy-aware Federated Learning (FL) framework called DR-FL to address the limitations of traditional FL methods in battery-driven scenarios. The “wooden barrel effect” refers to the mismatch between homogeneous model paradigms and heterogeneous device capability, leading to poor training performance and energy inefficiency. To overcome this challenge, DR-FL considers both clients’ energy constraints and deep learning models’ computing capabilities. It uses a Multi-Agents Reinforcement Learning (MARL)-based dual-selection method for effective knowledge sharing among diverse models while adhering to energy limitations. Experimental results demonstrate that DR-FL optimizes the exchange of knowledge in large-scale AIoT systems, improving individual device model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for devices with different capabilities to work together and learn from each other. However, when these devices are powered by batteries, it’s hard to get them to work well together because some devices might run out of energy before they can finish their tasks. This paper proposes a new way of doing federated learning that takes into account the energy constraints of these devices. It uses a special algorithm called MARL (Multi-Agents Reinforcement Learning) to help devices decide how much effort to put in and when to stop, based on their own energy levels and computing capabilities. The results show that this new approach can make federated learning more efficient and effective. |
Keywords
» Artificial intelligence » Deep learning » Federated learning » Reinforcement learning
