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Accelerating Optimization and Machine Learning through Decentralization
Ziqin Chen, Zuang Wang, Yongqiang Wang
Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need…
Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
Ziqin Chen, Yongqiang Wang
Distributed learning has gained significant attention due to its advantages in scalability, privacy, and fault tolerance.In this paradigm, multiple agents collaboratively train a g…
Privacy-Preserving Distributed Optimization and Learning
Ziqin Chen, Yongqiang Wang
Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite ra…
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs
Ziqin Chen, Yongqiang Wang
Distributed online learning has been proven extremely effective in solving large-scale machine learning problems over streaming data. However, information sharing between learners…