Federated Learning (FL), a privacy-oriented distributed ML paradigm, is being
gaining great interest in Internet of Things because of its capability to
protect participants data privacy. Studies have been conducted to address
challenges existing in standard FL, including communication efficiency and
privacy-preserving. But they cannot achieve the goal of making a tradeoff
between communication efficiency and model accuracy while guaranteeing privacy.
This paper proposes a Conditional Random Sampling (CRS) method and implements
it into the standard FL settings (CRS-FL) to tackle the above-mentioned
challenges. CRS explores a stochastic coefficient based on Poisson sampling to
achieve a higher probability of obtaining zero-gradient unbiasedly, and then
decreases the communication overhead effectively without model accuracy
degradation. Moreover, we dig out the relaxation Local Differential Privacy
(LDP) guarantee conditions of CRS theoretically. Extensive experiment results
indicate that (1) in communication efficiency, CRS-FL performs better than the
existing methods in metric accuracy per transmission byte without model
accuracy reduction in more than 7% sampling ratio (# sampling size / # model
size); (2) in privacy-preserving, CRS-FL achieves no accuracy reduction
compared with LDP baselines while holding the efficiency, even exceeding them
in model accuracy under more sampling ratio conditions