Summary of Federated Learning with Flexible Architectures, by Jong-ik Park and Carlee Joe-wong
Federated Learning with Flexible Architectures
by Jong-Ik Park, Carlee Joe-Wong
First submitted to arxiv on: 14 Jun 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 Federated Learning with Flexible Architectures (FedFA), an algorithm that addresses the limitations of traditional federated learning methods by allowing clients with varying computational and communication abilities to train models of different widths and depths. FedFA incorporates the layer grafting technique to align client local architectures with the largest network architecture during model aggregation, ensuring uniform integration of all client contributions into the global model. The algorithm also introduces scalable aggregation method to manage scale variations in weights among different network architectures. Experimentally, FedFA outperforms previous width and depth flexible aggregation strategies and demonstrates increased robustness against performance degradation in backdoor attack scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning with Flexible Architectures (FedFA) is a new way for devices with different computing powers to work together to train AI models. Right now, this can be a problem because some devices might not have enough power or resources to help with the training process. To fix this, FedFA lets each device choose its own network architecture based on what it has available, so shallower and thinner networks use fewer computing resources. This helps prevent one device’s data from dominating the model and makes it more secure. The algorithm also uses a special technique called layer grafting to make sure all devices’ contributions are combined in a fair way. Overall, FedFA is better than other methods at working with different network architectures and is more robust against attacks. |
Keywords
* Artificial intelligence * Federated learning
