Summary of Fedrewind: Rewinding Continual Model Exchange For Decentralized Federated Learning, by Luca Palazzo et al.
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning
by Luca Palazzo, Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Concetto Spampinato
First submitted to arxiv on: 14 Nov 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 The proposed FedRewind approach addresses data distribution shift in decentralized federated learning by leveraging model exchange among nodes. Inspired by continual learning principles and cognitive neuroscience theories for memory retention, FedRewind implements a decentralized routing mechanism to reduce spatial distribution challenges. During local training, nodes periodically send their models back (i.e., rewind) to the nodes they received them from for a limited number of iterations, enhancing learning and generalization performance. The method is evaluated on multiple benchmarks, demonstrating its superiority over standard decentralized federated learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedRewind is a new way to learn together with devices that don’t share their data. It’s like rewinding a tape to remember things better. This helps when the data is spread out in different ways, making it easier for all devices to learn and generalize. The results show that FedRewind works better than other methods. |
Keywords
» Artificial intelligence » Continual learning » Federated learning » Generalization
