OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning
This addresses energy efficiency for IoT and edge computing systems, though it is incremental as it builds on existing decentralized FL approaches.
The paper tackles communication costs and data heterogeneity in decentralized federated learning by proposing OCD-FL, a peer selection scheme that reduces energy consumption by 30-80% while achieving similar or better performance than fully collaborative methods.
The conjunction of edge intelligence and the ever-growing Internet-of-Things (IoT) network heralds a new era of collaborative machine learning, with federated learning (FL) emerging as the most prominent paradigm. With the growing interest in these learning schemes, researchers started addressing some of their most fundamental limitations. Indeed, conventional FL with a central aggregator presents a single point of failure and a network bottleneck. To bypass this issue, decentralized FL where nodes collaborate in a peer-to-peer network has been proposed. Despite the latter's efficiency, communication costs and data heterogeneity remain key challenges in decentralized FL. In this context, we propose a novel scheme, called opportunistic communication-efficient decentralized federated learning, a.k.a., OCD-FL, consisting of a systematic FL peer selection for collaboration, aiming to achieve maximum FL knowledge gain while reducing energy consumption. Experimental results demonstrate the capability of OCD-FL to achieve similar or better performances than the fully collaborative FL, while significantly reducing consumed energy by at least 30% and up to 80%.