arXiv subjects

Yuanxiong Guo

Publications and source records attributed to Yuanxiong Guo.

24 records · Page 2Linked to original sources

arXiv2021

Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not provide sufficient privacy protection, and it is desirable to facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP mechanisms would introduce random noise with magnitude proportional to the model size, which can be quite large in deep neural networks. In this paper, we propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee. Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost. We rigorously analyze the convergence of our approach and utilize Renyi DP to tightly account the end-to-end DP guarantee. Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency.

cs.LG↗

arXiv2020

Concentrated Differentially Private and Utility Preserving Federated Learning

Federated learning is a machine learning setting where a set of edge devices collaboratively train a model under the orchestration of a central server without sharing their local data. At each communication round of federated learning, edge devices perform multiple steps of stochastic gradient descent with their local data and then upload the computation results to the server for model update. During this process, the challenge of privacy leakage arises due to the information exchange between edge devices and the server when the server is not fully trusted. While some previous privacy-preserving mechanisms could readily be used for federated learning, they usually come at a high cost on convergence of the algorithm and utility of the learned model. In this paper, we develop a federated learning approach that addresses the privacy challenge without much degradation on model utility through a combination of local gradient perturbation, secure aggregation, and zero-concentrated differential privacy (zCDP). We provide a tight end-to-end privacy guarantee of our approach and analyze its theoretical convergence rates. Through extensive numerical experiments on real-world datasets, we demonstrate the effectiveness of our proposed method and show its superior trade-off between privacy and model utility.

cs.LG↗

arXiv2020

Differentially Private Federated Learning for Resource-Constrained Internet of Things

With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous data is being generated at the network edge. Federated learning is capable of analyzing the large amount of data from a distributed set of smart devices without requiring them to upload their data to a central place. However, the commonly-used federated learning algorithm is based on stochastic gradient descent (SGD) and not suitable for resource-constrained IoT environments due to its high communication resource requirement. Moreover, the privacy of sensitive data on smart devices has become a key concern and needs to be protected rigorously. This paper proposes a novel federated learning framework called DP-PASGD for training a machine learning model efficiently from the data stored across resource-constrained smart devices in IoT while guaranteeing differential privacy. The optimal schematic design of DP-PASGD that maximizes the learning performance while satisfying the limits on resource cost and privacy loss is formulated as an optimization problem, and an approximate solution method based on the convergence analysis of DP-PASGD is developed to solve the optimization problem efficiently. Numerical results based on real-world datasets verify the effectiveness of the proposed DP-PASGD scheme.

cs.LG↗

arXiv2019

DP-ADMM: ADMM-based Distributed Learning with Differential Privacy

Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing. Such an iterative process could cause privacy concerns of data owners. The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning. Prior approaches on differentially private ADMM exhibit low utility under high privacy guarantee and often assume the objective functions of the learning problems to be smooth and strongly convex. To address these concerns, we propose a novel differentially private ADMM-based distributed learning algorithm called DP-ADMM, which combines an approximate augmented Lagrangian function with time-varying Gaussian noise addition in the iterative process to achieve higher utility for general objective functions under the same differential privacy guarantee. We also apply the moments accountant method to bound the end-to-end privacy loss. The theoretical analysis shows that DP-ADMM can be applied to a wider class of distributed learning problems, is provably convergent, and offers an explicit utility-privacy tradeoff. To our knowledge, this is the first paper to provide explicit convergence and utility properties for differentially private ADMM-based distributed learning algorithms. The evaluation results demonstrate that our approach can achieve good convergence and model accuracy under high end-to-end differential privacy guarantee.

cs.LG↗

arXiv2018

Smart Charging and Parking of Plug-in Hybrid Electric Vehicles in Microgrids Considering Renewable Energy Sources

With the current trend of transforming a centralized power system into a decentralized one for efficiency, reliability, and environment reasons, the concept of microgrid that integrates a variety of distributed energy resources (DERs) on the distribution network is gaining popularity. In this paper, we investigate the smart charging and parking of plug-in hybrid electric vehicles (PHEVs) in microgrids with renewable energy sources (RES), such as solar panels, in grid-connected mode. To address the uncertainties associated with RES power output and PHEVs charging condition in the microgrid, we propose a two-stage scenario-based stochastic optimization approach with the objective of providing a proper scheduling for parking and charging of PHEVs that minimizes the average total operating cost while maintaining the reliability of the microgrid. A case study is conducted to show the effectiveness of the proposed approach. Extensive simulation results show that the microgrid can minimize the operating cost and ensure its reliability.

math.OC↗

arXiv2017

Aggregation-Based Datacenter Energy Management in Wholesale Electricity Markets

In this paper, we study how datacenter energy cost can be effectively reduced in the wholesale electricity market via cooperative power procurement. Intuitively, by aggregating workloads and renewables across a group of datacenters, the overall power demand uncertainty of datacenters can be reduced, resulting in less chance of being penalized when participating in the wholesale electricity market. We use cooperative game theory to model the cooperative electricity procurement process of datacenters as a cooperative game, and show the cost saving benefits of aggregation. Then, a cost allocation scheme based on the marginal contribution of each datacenter to the total expected cost is proposed to distribute the aggregation benefits among the participating datacenters. Besides, we propose proportional cost allocation scheme to distribute the aggregation benefits among the participating datacenters after realizations of power demand and market prices. Finally, numerical experiments based on real-world traces are conducted to illustrate the benefits of aggregation compared to noncooperative power procurement.

cs.GT↗