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This paper addresses the challenges of training latency and convergence degradation in federated learning (FL) over unreliable wireless networks, particularly in blocked propagation environments. By leveraging reconfigurable intelligent surfaces (RISs), the authors characterize the trade-off between learning convergence and communication delay, deriving a convergence-related upper bound that quantifies the impact of symbol error rates on FL loss decay. The proposed adaptive modulation and resource allocation strategy, formulated as a mixed-integer nonlinear programming problem, significantly enhances convergence speed and test accuracy across various datasets compared to existing methods.
Adaptive modulation in RIS-assisted federated learning can drastically improve convergence speed and accuracy, even in challenging wireless environments.
Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable intelligent surfaces (RISs) can improve communication reliability, existing wireless FL studies rarely characterize the trade-off between learning convergence and communication delay under modulation-dependent transmission errors. In this paper, we consider a wireless FL system operating under RIS-assisted blocked-link propagation scenarios, and focus on adaptive modulation and sub-channel allocation for convergence-latency aware communication design. By characterizing the effect of symbol errors on uploaded local gradients, we derive a convergence-related upper bound that reveals the impact of symbol error rate (SER) on FL loss decay. Based on this result, we formulate a joint convergence-latency optimization problem, which is cast as a mixed-integer nonlinear programming (MINLP) problem, and solve it using a low-complexity hybrid alternating optimization framework. Extensive experiments on MNIST, CIFAR-10, and Speech Commands show that the proposed scheme consistently achieves faster convergence and higher test accuracy than existing adaptive communication schemes, especially in complex tasks and challenging wireless scenarios.
