Summary of Load Balancing in Federated Learning, by Alireza Javani and Zhiying Wang
Load Balancing in Federated Learning
by Alireza Javani, Zhiying Wang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 A novel Federated Learning (FL) scheduling policy is proposed to address challenges in distributing workload among multiple edge devices. The Age of Information-based load metric is introduced to minimize variance across clients, ensuring fair and efficient resource utilization. A decentralized Markov scheduling policy is also presented, allowing for balanced workload distribution and eliminating management overhead. Simulation results demonstrate the effectiveness of reducing load metric variance in promoting fairness, improving operational efficiency, and enhancing convergence rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps devices share knowledge without sharing data. The problem is that some devices might get more work than others, wasting resources. This paper solves this by using a new way to measure how much work each device needs. It also introduces a scheduling system that lets devices make their own decisions about when to do their part of the learning process. This makes it easier and faster for devices to work together. |
Keywords
» Artificial intelligence » Federated learning
