Pigeon-SL: Robust Split Learning Framework for Edge Intelligence under Malicious Clients

This work has been submitted to the IEEE for possible publication. Abstract --Recent advances in split learning (SL) have established it as a promising framework for privacy-preserving, communication-efficient distributed learning at the network edge. However, SL's sequential update process is vulnerable to even a single malicious client, which can significantly degrade model accuracy. T o address this, we introduce Pigeon-SL, a novel scheme grounded in the pigeonhole principle that guarantees at least one entirely honest cluster among M clients, even when up to N of them are adversarial. In each global round, the access point partitions the clients into N + 1 clusters, trains each cluster independently via vanilla SL, and evaluates their validation losses on a shared dataset. We further enhance training and communication efficiency with Pigeon-SL+, which repeats training on the selected cluster to match the update throughput of standard SL.