Let's Federate - Effective Communication Strategy for Dynamic Client Participation

Federated Learning (FL) has emerged as a privacy-preserving powerful tool in decentralized Machine Learning (ML) environments. However, real-world scenarios often face bandwidth limitations that can be overwhelmed when all clients simultaneously perform training and communicate with the server in a federated system. Consequently, selection mechanisms are critical for identifying optimal subsets of clients to participate in the federation. Traditional selection methods, however, typically do not allow clients the autonomy to decide whether or not to contribute to the federation. Therefore, this paper proposes LetsFed, a client selection framework that respects client independence throughout the training process. The LetsFed framework differentiates between participating and non-participating clients, employing targeted selection mechanisms to address system challenges effectively. Empirical results demonstrate that LestFed can outperform, in dynamic client participation environments, literature solutions by up to 40%, reducing unnecessary data transmission by as much as 29%, while also enhancing the efficacy of the selection process.