sqSGD: Locally Private and Communication Efficient Federated Learning. (arXiv:2206.10565v2 [cs.LG] UPDATED)

Federated learning (FL) is a technique that trains machine learning models
from decentralized data sources. We study FL under local notions of privacy
constraints, which provides strong protection against sensitive data
disclosures via obfuscating the data before leaving the client. We identify two
major concerns in designing practical privacy-preserving FL algorithms:
communication efficiency and high-dimensional compatibility. We then develop a
gradient-based learning algorithm called \emph{sqSGD} (selective quantized
stochastic gradient descent) that addresses both concerns. The proposed
algorithm is based on …

communication lg