Summary of Risk-aware Accelerated Wireless Federated Learning with Heterogeneous Clients, by Mohamed Ads et al.
Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
by Mohamed Ads, Hesham ElSawy, Hossam S. Hassanein
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Wireless Federated Learning (FL) is a distributed machine learning paradigm that enables confidential data sharing among mobile clients. However, location-dependent performance issues hinder its convergence speed and accuracy. To address this challenge, our novel risk-aware accelerated FL framework considers client heterogeneity in terms of data amount, transmission rates, errors, and trustworthiness. We propose a dynamic scheme that aggregates global models based on clients’ transmission rates and trustworthiness profiles. Initially, the transmission rate dominates to accelerate convergence speed, then relaxes to explore more training data at cell-edge clients. Our scheme incorporates debiasing factors for transmission errors and employs a validation set to eliminate non-trustworthy clients during fine-tuning. We compare our proposed scheme with conservative and aggressive benchmarks, demonstrating superior accuracy and convergence speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way for mobile devices to work together to learn new things, even when they’re not connected to the internet. This is called Wireless Federated Learning (FL). The problem is that some devices might be in bad locations or have poor connections, which makes it hard for them to contribute to the learning process. To solve this issue, we developed a new approach that takes into account how good each device’s connection and trustworthiness are. We show that our method can learn faster and more accurately than other methods, even when some devices aren’t very reliable. |
Keywords
* Artificial intelligence * Federated learning * Fine tuning * Machine learning
