  Federated Learning enables diverse devices to collaboratively train a shared
model while keeping training data locally stored, avoiding the need for
centralized cloud storage. Despite existing privacy measures, concerns arise
from potential reverse engineering of gradients, even with added noise,
revealing private data. To address this, recent research emphasizes using
encrypted model parameters during training. This paper introduces a novel
federated learning algorithm, leveraging coded local gradients without
encryption, exchanging coded proxies for model parameters, and injecting
surplus noise for enhanced privacy. Two algorithm variants are presented,
showcasing convergence and learning rates adaptable to coding schemes and raw
data characteristics. Two encryption-free implementations with fixed and random
coding matrices are provided, demonstrating promising simulation results from
both federated optimization and machine learning perspectives.
