Summary of Trail: Trust-aware Client Scheduling For Semi-decentralized Federated Learning, by Gangqiang Hu et al.
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by Gangqiang Hu, Jianfeng Lu, Jianmin Han, Shuqin Cao, Jing Liu, Hao Fu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 a Trust-Aware client scheduling mechanism called TRAIL to tackle the dynamic challenges inherent in real-world semi-decentralized Federated Learning (FL) scenarios. The proposed method assesses client states and contributions, enhancing model training efficiency through selective client participation. Specifically, the authors propose an adaptive hidden semi-Markov model to estimate clients’ communication states and contributions, followed by a greedy client scheduling algorithm. Experimental results on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to make sure that machines can learn from lots of different devices without sharing their personal data. This is important because some devices might have really sensitive information on them. The researchers created a special schedule for the devices so that they only share what’s necessary and don’t waste time or resources. They used a special kind of math called hidden semi-Markov models to figure out which devices are trustworthy and should be allowed to participate in the learning process. This new approach worked really well and was able to make better predictions than previous methods. |
Keywords
» Artificial intelligence » Federated learning » Markov model
