Summary of Ocd-fl: a Novel Communication-efficient Peer Selection-based Decentralized Federated Learning, by Nizar Masmoudi et al.
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning
by Nizar Masmoudi, Wael Jaafar
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel approach to decentralized federated learning (FL), called opportunistic communication-efficient decentralized federated learning (OCD-FL). Conventional FL with a central aggregator presents a single point of failure and network bottleneck. To address this, OCD-FL selects peers for collaboration based on a systematic FL peer selection algorithm, aiming to maximize knowledge gain while reducing energy consumption. The proposed scheme achieves similar or better performances than fully collaborative FL, while significantly reducing consumed energy by at least 30% and up to 80%. This is particularly relevant in the context of edge intelligence and the Internet-of-Things (IoT) network, where decentralized FL has emerged as a prominent paradigm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine learning work better for devices connected to the internet. One problem with this type of learning is that it can be slow or use too much energy. The researchers propose a new way to do this, called OCD-FL, which helps devices learn from each other while using less energy. This approach works just as well as the old method, but uses 30% to 80% less energy. This could help make it possible for many more devices to work together and share information. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
