private federated learning: an adversarial sanitizing perspective

Large-scale data collection is challenging in alternative centralized learning as privacy concerns or prohibitive policies may rise. as a solution, federated learning (fl) is proposed wherein data owners, called participants, can train a common model collaboratively while their privacy is preserved. however, recent attacks, namely membership inference attacks (mia) or poisoning attacks (pa), can threaten the privacy and performance in fl systems. this paper develops an innovative adversarial-re silient privacy-preserving scheme (arps) for fl to cope with preceding threats using differential privacy and cryptography. our experiments display that arps can establish a private model with high accuracy outperforming state-of-the-art approaches. to the best of our knowledge, this work is the only scheme providing privacy protection beyond any output models in conjunction with byzantine resiliency without sacrificing accuracy and efficiency.