Summary of Fair Concurrent Training Of Multiple Models in Federated Learning, by Marie Siew et al.
Fair Concurrent Training of Multiple Models in Federated Learning
by Marie Siew, Haoran Zhang, Jong-Ik Park, Yuezhou Liu, Yichen Ruan, Lili Su, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong
First submitted to arxiv on: 22 Apr 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 In this paper, researchers propose a novel approach to federated learning that enables simultaneous training of multiple learning tasks across clients while ensuring fair performance. The proposed Multiple-Model Federated Learning (MMFL) framework addresses the challenges of naive client-task allocation schemes that can lead to unfair outcomes due to heterogeneous task difficulty levels. To overcome these limitations, the authors introduce FedFairMMFL, a difficulty-aware algorithm that dynamically allocates clients to tasks and provides guarantees on fairness and convergence rate. Additionally, they propose an auction-based mechanism to incentivize clients to train multiple tasks, resulting in a fair distribution of training efforts. The proposed approach is evaluated using real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make it easier for many devices to work together to learn new things. They want to do this with lots of different learning tasks at the same time, which can be tricky because some tasks might need more help or resources than others. The researchers came up with a special way to share the workload fairly and efficiently, so that everyone gets what they need. They also created a system that motivates devices to participate in all the learning tasks, rather than just picking their favorites. This new approach can be used for real-world problems. |
Keywords
» Artificial intelligence » Federated learning
