Asynchronous Federated Learning with Differential Privacy for Edge Intelligence

Abstract--Federated learning has been showing as a promising approac h in paving the last mile of artificial intelligence, due to it s great potential of solving the data isolation problem in lar ge scale machine learning. Particularly, with considerati on of the heterogeneity in practical edge computing systems, asynchronous edge-cl oud collaboration based federated learning can further imp rove the learning efficiency by significantly reducing the straggler effect. Despite no raw data sharing, the open architecture a nd extensive collaborations of asynchronous federated learning (AFL) s till give some malicious participants great opportunities to infer other parties' training data, thus leading to serious concerns of privacy . T o achieve a rigorous privacy guarantee with high utility, w e investigate to secure asynchronous edge-cloud collaborative federated l earning with differential privacy, focusing on the impacts of differential privacy on model convergence of AFL. Formally, we give the first analy sis on the model convergence of AFL under DP and propose a multistage adjustable private algorithm (MAP A) to improv e the tradeoff between model utility and privacy by dynamic ally adjusting both the noise scale and the learning rate. Through extensiv e simulations and real-world experiments with an edge-coul d testbed, we demonstrate that MAP A significantly improves both the model accuracy and convergence speed with sufficient privacy guar antee. Index Terms --Distributed machine learning, Federated learning, Async hronous learning, Differential privacy, Convergence. However, with the increasing public awareness of privacy, more and more people are reluctant to provide their own data [7]- [9]. At the same time, large companies or organizations also begin to realize that the curated data is their coral assets with abundant business value [10], [11].