Clustered Federated Learning to Support Context-Dependent CSI Decoding

Neural network-based encoders and decoders have demonstrated significant performance gains over traditional methods for Channel State Information (CSI) feedback in MIMO communications. However, key challenges in deploying these models in real-world scenarios remain underexplored, including: (a) the need to efficiently accommodate diverse channel conditions across varying contexts, e.g., environments, and whether to use multiple encoders and decoders; (b) the cost of gathering sufficient data to train neural network models across various contexts; and (c) the need to protect sensitive data regarding competing providers’ coverages. To address the first challenge, we propose a novel system using context-dependent decoders and a universal encoder. We limit the number of decoders by clustering similar contexts and allowing those within a cluster to share the same decoder. To address the second and third challenges, we introduce a clustered federated learning-based approach that jointly clusters contexts and learns the desired encoder and context cluster-dependent decoders, leveraging distributed data. The clustering is performed efficiently based on the similarity of time-averaged gradients across contexts. To evaluate our approach, a new dataset reflecting the heterogeneous nature of the wireless systems was curated and made publicly available. Extensive experimental results demonstrate that our proposed CSI compression framework is highly effective and able to efficiently determine a correct context clustering and associated encoder and decoders.