We consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just $1$ bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.
Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Bologna（博洛尼亚大学）；KTH Royal Institute of Technology（皇家理工学院）；The Hong Kong University of Science and Technology（香港科技大学）
Distributed Optimization with Nesterov Momentum and Finite-Time Quantized Averaging over Directed Networks: Extended Version
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；KTH Royal Institute of Technology（皇家理工学院）
Fast-Convergent Dynamics for Distributed Allocation of Resources Over Switching Sparse Networks with Quantized Communication Links
Aalto University（阿尔托大学）；Semnan University（塞姆南大学）；University of Cyprus（塞浦路斯大学）；Georgia State University（佐治亚州立大学）；University of Waterloo（滑铁卢大学）；City University of Hong Kong（香港城市大学）；Tufts University（塔夫茨大学）
Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression
Zhejiang University（浙江大学）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）；National Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）
