# CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface Source: [CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface]() Collection index: [llms.txt]() Data updated: 2025-10-25T17:41:26.747Z Authors: Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang Published: 2021-02-22T03:24:23.000Z ## Abstract We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()