Hybrid IoT Device Selection With Knowledge Transfer for Federated Learning

Federated learning (FL) enables collaborative model training across massively distributed edge devices, such as Internet of Things (IoT) nodes. However, resource constraints impose a major challenge, as there exists a trade-off between maximizing learning accuracy and minimizing communication overhead between the resource-limited devices. In this paper, we present a device selection approach for heterogeneous FL systems based on multi-objective optimization and knowledge transfer. We formulate the resource constraint in federated optimization as a multi-objective problem, and obtain Pareto-optimal solutions balancing resource efficiency and test accuracy. Additionally, we introduce an innovative knowledge transfer mechanism that propagates the globally optimal models obtained during multi-objective optimization to subsequent FL tasks, further expediting convergence. The multi-objective formulation and knowledge transfer provide new insights into efficient and robust federated learning for resource-constrained IoT applications. We conduct extensive experiments on real-world datasets. Results demonstrate that our method achieves up to 11% higher accuracy than state-of-the-art methods, while effectively mitigating resource constraints. Impact Statement–Federated learning is an efficient algorithm that enables everything to be interconnected without sharing data. However, resource constraint is the main challenge for federated optimization problems. Although many works have proposed various solutions from different perspectives, these methods cannot simultaneously minimize the communication resource cost while ensuring algorithm performance. We propose an automatic device selection algorithm for federated systems based on multi-objective optimization and knowledge transfer. This work not only reduces the global resource usage rate of federated learning, but also enables it to converge quickly.