Federated Learning Client Selection with Data Fairness and Reputation

In this article, we explore the problem of client selection in federated learning (FL) systems, where a job requires multiple datasets to be contributed by different clients. The author presents a novel approach called FairFedJS, which aims to select clients that contribute the most value to the job while ensuring fairness among all clients.
The article begins by highlighting the complexity of client selection in FL systems, where the job requires multiple datasets from different clients. The author notes that traditional approaches rely on simplistic criteria such as the number of available datasets or the size of the datasets, which can lead to unfair and inefficient client selection. To address this issue, the author proposes FairFedJS, a novel approach that takes into account both the reputation of clients and their fairness in contributing data.
The author explains that FairFedJS is built upon the Beta Reputation System (BRS), which manages a distinct client reputation table for each specific data type. The BRS calculates the reputation of each client based on their past contributions to jobs, and the client with the highest reputation is selected for each job. However, this approach can lead to unfairness among clients, as some clients may have higher reputations than others due to factors beyond their control. To address this issue, FairFedJS introduces a new criterion called fairness, which ensures that all clients are treated fairly and have an equal chance of being selected for each job.
The author demonstrates the effectiveness of FairFedJS through simulations and theoretical analysis. The results show that FairFedJS can significantly improve the efficiency and fairness of client selection in FL systems compared to traditional approaches. Additionally, the author shows that FairFedJS can be applied to various FL scenarios, including horizontal FL where a job requires multiple datasets from different clients.
In summary, this article presents FairFedJS, a novel approach to client selection in federated learning systems that ensures both efficiency and fairness among all clients. By taking into account the reputation of clients and their fairness in contributing data, FairFedJS can significantly improve the performance of FL systems. The author demonstrates the effectiveness of FairFedJS through simulations and theoretical analysis, highlighting its potential to revolutionize the field of FL.