HEFLGuard: Backdoor Detection in Homomorphic Encryption-Based Federated Learning

Homomorphic encryption-based federated learning (HEFL) strengthens privacy by aggregating encrypted model updates, but it also renders existing backdoor defenses that assume plaintext updates inapplicable. We present HEFLGuard, a single-server backdoor detection framework for HEFL in which the server constructs overlapping validation models from encrypted client groups and clients locally compare logits of the global and validation models on benign samples to expose backdoor behavior. HEFLGuard further combines consistency verification across non-IID validation groups with Byzantine fault-tolerant aggregation of client reports, ensuring robustness under heterogeneous data and Byzantine participants. We evaluate HEFLGuard on seven vision/text benchmarks under three backdoor types across IID and non-IID settings. HEFLGuard consistently reduces ASR from near 100% to nearly the nobackdoor level while keeping the drop in clean accuracy within 2.5%. Compared with prior work, HEFLGuard achieves higher robustness and deployability.