LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping

The recent introduction of LSHFed marks a significant advancement in federated learning, a method that allows for collaborative model training while keeping data secure. This new approach addresses vulnerabilities in decentralized systems, particularly against inference and poisoning attacks, which can compromise sensitive information and model integrity. By enhancing communication efficiency and reducing computational costs, LSHFed not only strengthens data privacy but also paves the way for more robust applications in trust-deficient environments, making it a noteworthy development in the field.