Ji Liu · PaperMetrix

Marginal Policy Gradients for Complex Control.

2018 · arXiv (Cornell University)

Many complex domains, such as robotics control and real-time strategy (RTS) games, require an agent to learn a continuous control. In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over …

Central Server Free Federated Learning over Single-sided Trust Social Networks

2019 · arXiv (Cornell University)

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated …

Improving Certified Robustness via Statistical Learning with Logical Reasoning

2020 · arXiv (Cornell University)

Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given …

Federated Fingerprint Learning with Heterogeneous Architectures

2022 · 2022 IEEE International Conference on Data Mining (ICDM)

Recent studies on federated learning (FL) have sought to solve the system heterogeneity issue by designing customized local models for different clients. However, public dataset introduction, sensitive information exchange, non-trivial computational cost, or particular architecture …

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning

2024 · arXiv (Cornell University)

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, …

Dynamic Resource Allocation with Quantum Error Detection