Federated learning has received significant interests recently due to its capability of learning a shared machine learning model across smart devices without accessing their private data in the era of Internet of things. This paper jointly considers two critical issues of federated learning -- data privacy and communication efficiency -- and develops a communication-efficient and differentially-private federated learning scheme called CPFed. The main challenge in addressing both issues together lies in the fact that data compression techniques often lead to an increased number of training iterations required for achieving some desired training loss due to the compression errors, while the differential privacy guarantee usually deteriorates with respect to the number of training iterations. To reconcile this dilemma, we propose to use sparsified privacy-masking that first adds random noise to the model update and then applies unbiased random sparsifier before uploading the model update at each device in federated learning. By using sparsified privacy-masking, our proposed CPFed scheme can achieve high communication efficiency and strong data privacy guarantee at the same time while preserving model accuracy. We provide an explict end-to-end privacy guarantee of CPFed using zero-concentrated differential privacy and give its theoretical convergence rates for both convex and non-convex models. Through extensive numerical experiments on real-world datasets, we demonstrate the effectiveness and efficiency of our proposed method.

Federated learning is a machine learning setting where a set of edge devices iteratively train a model under the orchestration of a central server, while keeping all data locally on edge devices. In each iteration of federated learning, edge devices perform computation with their local data, and the local computation results are then uploaded to the server for model update. During this process, the challenges of privacy leakage and communication overhead arise due to the extensive information exchange between edge devices and the server. In this paper, we develop CPFed, a communication-efficient and privacy-preserving federated learning method, to solve the above challenges. CPFed integrates three key components: (1) periodic averaging where local computation results at edge devices are only periodically averaged at the server; (2) Gaussian mechanism where edge devices randomly perturb their local computation results before sending the results to the server; and (3) secure aggregation where the perturbed local computation results are homomorphically encrypted before being sent to the server. CPFed can address both the communication efficiency and privacy leakage challenges in federated learning while achieving high model accuracy. We provide an end-to-end privacy guarantee of CPFed and analyze its theoretical convergence rates for both convex and non-convex models. Through extensive numerical experiments on real-world datasets, we demonstrate the effectiveness and efficiency of our proposed method.