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Phd Thesis Proposal - Chaoyang He

Ph.D. Candidate: Chaoyang He

Title: Towards End-to-end Federated Machine Learning at Scale: Algorithm, Systems, and Applications



Committee members: Prof. Salman Avestimehr (Chair), Prof. Mahdi Soltanolkotabi, Prof. Murali Annavaram, Prof. Xiang Ren, Prof. Barath Raghavan

Abstract: Federated learning (FL) is a machine learning paradigm that many clients (e.g. mobile/IoT devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. It has shown huge potential in mitigating many of the systemic privacy risks, regulatory restrictions, and communication costs, resulting from traditional, centralized machine learning and data science approaches in healthcare, finance, smart city, autonomous driving, and the Internet of things. Though it's promising, landing FL into trustworthy data-centric AI infrastructure faces many realistic challenges from learning algorithms (e.g., data heterogeneity, label deficiency) and distributed systems (resource constraints, system heterogeneity, security, privacy, etc.), requiring interdisciplinary research in machine learning, distributed systems, and security/privacy. Driven by this goal, My Ph.D. research focuses on end-to-end FL research, from algorithms to systems to applications. In this thesis proposal, I will first summarize my publications from the perspective of FedML, a widely adopted open-source library I developed, and highlight how I enable scale FL at cross-device and cross-silo settings, as well as diverse applications in CV, NLP, and data mining. Second, I will also introduce my broader accomplishment, including visionary papers, open-source impacts, academia service, industrial collaboration, invited talks, and workshop organization. Finally, I will briefly introduce my ongoing works on secure aggregation and label deficiency and finalize my presentation with a clear future plan.

Zoom Link: https://usc.zoom.us/my/usc.chaoyanghe

WebCast Link: https://usc.zoom.us/my/usc.chaoyanghe

Audiences: Everyone Is Invited

Contact: Lizsl De Leon