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Ada-FFL:adaptive computing fairness federated learning

CAAI Transactions on Intelligence Technology 2024年第3期9卷 573-584页

作者： Yue Cong Jing Qiu Kun Zhang Zhongyang Fang Chengliang Gao Shen Su Zhihong Tian The Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Pengcheng Lab ShenzhenChina Carnegie Mellon University PittsburghPennsylvaniaUSA Mohamed Bin Zayed University of Artificial Intelligence Abu DhabiUnited Arab Emirates

As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of *** existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less *** fairness may greatly reduce the willingness of some clients to participate in federated *** order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning *** integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of ***,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be *** numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

关键词： adaptive fariness aggregation fairness federated learning non-IID

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