Robust Privacy-Preserving Federated Learning for Edge Computing With New Client Integration

Federated learning (FL) is a key paradigm for deploying AI models across large numbers of Internet of Things (IoT) devices in edge computing. While FL avoids uploading raw data to a central server, client privacy remains vulnerable during new client integration, when previously unseen devices first register their identities and cryptographic keys. A malicious or semi-honest central server (CS) can manipulate training to isolate target gradients, reconstruct local data, and tamper with aggregation. We study the Identity Forgery and Gradient Inversion Attack (IFGIA) against federated edge learning. By fabricating virtual clients and exploiting secure aggregation, a malicious CS can recover target gradients with success rates above 99.5% under realistic edge settings, revealing a critical weakness in existing privacy-preserving and verifiable FL schemes. To defend against IFGIA, we propose Robust Federated Learning (RFL), a framework tailored for edge computing that combines model splitting between edge clients and edge servers, lightweight differential privacy on intermediate representations, and split verification using digital signatures and homomorphic hashes. Experiments show that RFL reduces IFGIA's success rate to 73.6%, shrinks per-client communication from 375 MB to 40 KB, accelerates edge-side training by at least 10×, and maintains competitive accuracy.