Resource-Efficient Federated Learning
원문 초록
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.