Reshmi Mitra

Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space

Federated Learning (FL) facilitates clients to collaborate on training a shared machine learning model without exposing individual private data. Nonetheless FL remains susceptible to utility and privacy attacks notably evasion data poisoning and model inversion attacks compromising the system's effi…