dc.contributor.author | Gauthier, Francois Jean Rene | |
dc.contributor.author | Gogineni, Vinay Chakravarthi | |
dc.contributor.author | Werner, Anders Stefan | |
dc.date.accessioned | 2023-11-17T15:39:57Z | |
dc.date.available | 2023-11-17T15:39:57Z | |
dc.date.created | 2023-11-10T10:51:17Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1550-3607 | |
dc.identifier.uri | https://hdl.handle.net/11250/3103328 | |
dc.description.abstract | Personalized federated learning enables every edge device or group of edge devices within the distributed network to learn a device- or cluster-specific model tailored to their local needs. Data scarcity, however, makes it difficult to learn such individual models, resulting in performance degradation. Since the device- or cluster-specific tasks are distinct but often related, leveraging these similarities through inter-cluster learning alleviates data shortage and enhances learning performance. Although inter-cluster learning can boost performance, uncontrolled intercluster learning may lead to performance degradation due to over- or under-usage of local similarity enforcement. In light of this issue, an intelligent mechanism that performs inter-cluster learning based on device-specific needs is required. To this end, this paper proposes adopting reinforcement learning principles to control device-specific inter-cluster learning in real-time. We propose networked personalized federated learning using reinforcement learning (NPFed-RL) as a general framework and then demonstrate its feasibility by applying it to the ridge regression problem. We conduct numerical experiments to compare the proposed method with the state-of-the-art. The proposed method successfully controls device-specific parameters and offers better learning performance than existing solutions. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Networked personalized federated learning using reinforcement learning | en_US |
dc.title.alternative | Networked personalized federated learning using reinforcement learning | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © Copyright 2023 IEEE - All rights reserved. | en_US |
dc.source.journal | IEEE International Conference on Communications | en_US |
dc.identifier.doi | 10.1109/ICC45041.2023.10279781 | |
dc.identifier.cristin | 2194973 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |