Greedy Shapley Client Selection for Communication-Efficient Federated Learning
This work addresses communication efficiency for federated learning applications with timing constraints, representing an incremental improvement over standard unbiased selection methods.
The paper tackles the problem of slow convergence in federated learning under client heterogeneity and communication constraints by developing GreedyFed, a biased client selection strategy that greedily picks the most contributing clients, which demonstrates fast and stable convergence with high accuracy on real-world datasets.
The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast convergence under practical settings characterized by significant heterogeneity in data distribution, computing, and communication resources across clients. For applications having timing constraints due to limited communication opportunities with the parameter server (PS), the client selection strategy is critical to complete model training within the fixed budget of communication rounds. To address this, we develop a biased client selection strategy, GreedyFed, that identifies and greedily selects the most contributing clients in each communication round. This method builds on a fast approximation algorithm for the Shapley Value at the PS, making the computation tractable for real-world applications with many clients. Compared to various client selection strategies on several real-world datasets, GreedyFed demonstrates fast and stable convergence with high accuracy under timing constraints and when imposing a higher degree of heterogeneity in data distribution, systems constraints, and privacy requirements.