Summary of Bacsa: a Bias-aware Client Selection Algorithm For Privacy-preserving Federated Learning in Wireless Healthcare Networks, by Sushilkumar Yadav and Irem Bor-yaliniz
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
by Sushilkumar Yadav, Irem Bor-Yaliniz
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 Federated Learning has emerged as a transformative approach in healthcare, enabling collaborative model training across decentralized data sources while preserving user privacy. However, performance degrades due to inherent bias in non-IID data among participating clients, posing challenges to model accuracy and generalization. To address this issue, we propose the Bias-Aware Client Selection Algorithm (BACSA), which detects user bias and strategically selects clients based on their bias profiles. Our approach begins with a novel method for detecting user bias by analyzing model parameters and correlating them with class-specific data samples. We then formulate a mixed-integer non-linear client selection problem leveraging detected bias, alongside wireless network constraints, to optimize FL performance. Evaluations show that BACSA improves convergence and accuracy compared to existing benchmarks through evaluations on various data distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In healthcare, Federated Learning helps train models without sharing private data. However, this approach often fails when the data is biased or unfair. To fix this issue, we created an algorithm called Bias-Aware Client Selection Algorithm (BACSA). It identifies bias and chooses which devices to use for training based on that information. Our method starts by finding bias in model parameters and comparing it to class-specific data samples. Then, we make a complex problem-solving formula that uses detected bias and network constraints to improve FL performance. By using this algorithm, we showed that it can train models faster and better than other methods. |
Keywords
» Artificial intelligence » Federated learning » Generalization
