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In classical federated learning, the clients contribute to the overalltraining by communicating local updates for the underlying model on theirprivate data to a coordinating server. However, updating and communicating theentire model becomes prohibitively expensive when resource-constrained clientscollectively aim to train a large machine learning model. Split learningprovides a natural solution in such a setting, where only a small part of themodel is stored and trained on clients while the remaining large part of themodel only stays at the servers. However, the model partitioning employed insplit learning introduces a significant amount of communication cost. Thispaper addresses this issue by compressing the additional communication using anovel clustering scheme accompanied by a gradient correction method. Extensiveempirical evaluations on image and text benchmarks show that the proposedmethod can achieve up to $490\times$ communication cost reduction with minimaldrop in accuracy, and enables a desirable performance vs. communicationtrade-off.

FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients