Nowadays, the sheer amount of data collected from edge devices such as mobile phones and self-driving vehicles is beginning to overwhelm traditional centralized data analytics regimes where data from the edge is continuously uploaded to a central server to be processed. Excessive communication traffic from data upload, significant central server storage needs, energy expenditures from centralized learning of big data models, and privacy concerns from sharing raw data are becoming critical challenges in centralized systems. Fortunately, a critical change is happening in today’s Internet of Things (IoT). The processing and computational power of edge devices is becoming increasingly powerful. AI chips are rapidly infiltrating the global market. As such, we now have the opportunity to process more of our data where it is created - i.e., at the edge. This decentralized data analytics paradigm is often coined as federated data analytics (FDA). FDA resolves many of the aforementioned drawbacks. By exploiting edge computations, one can parallelize inference, reduce storage and communication costs, achieve faster alerts and decisions, and protect privacy, amongst many others. Meanwhile, FDA, as an emerging technology, poses significant intellectual challenges. To name a few: (1) most FDA work focuses on deep neural networks and empirical risk minimization (ERM). However, statistical questions such as variable selection, uncertainty quantification, hypothesis testing, and incorporating domain expert knowledge remain unanswered in FDA; (2) edge devices often have local datasets that differ in both size and distribution. Most FDA papers learn a single global model and fail to provide reasonable predictions when heterogeneity exists; (3) IoT systems can raise bias and fairness concerns. Devices with insufficient amounts of data, limited bandwidth, or unreliable internet connection are not favored by conventional training algorithms.
In this talk, I will present my two research papers that address the aforementioned challenges in FDA. First, I will present the federated Gaussian process that provides solutions that go beyond ERM and to correlated settings, which is very common in engineering situations. Interestingly, this model can naturally handle statistical heterogeneity and provide a personalized solution to each edge device. Second, I will present a framework – GIFAIR-FL that imposes group and individual fairness to the FDA setting. The talk concludes with some interesting future directions and my recent work on the collaborative process parameter design and its application in 3D printing


Presenter Bio:
Xubo Yue is a Ph.D. candidate in the Department of Industrial & Operations Engineering at the University of Michigan. His research focuses on federated and distributed data analytics. Currently, he is developing federated data analytics methods that rethink how both prescriptive and predictive analytics are done within IoT-enabled systems, specifically manufacturing and renewable energy. He has received several best paper awards from the Institute for Operations Research and the Management Sciences (INFORMS), the Institute of Industrial and Systems Engineers (IISE), and other renowned organizations.