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In distributed optimization schemes consisting of a group of agents connected to a central coordinator, the optimization algorithm often involves the agents solving private local sub-problems and exchanging data frequently with the coordinator to solve the global distributed problem. In those cases, the query-response mechanism usually causes excessive communication costs to the system, necessitating communication reduction in scenarios where communication is costly. Integrating Gaussian processes (GP) as a learning component to the Alternating Direction Method of Multipliers (ADMM) has proven effective in learning each agent’s local proximal operator to reduce the required communication exchange. A key element for integrating GP into the ADMM algorithm is the querying mechanism upon which the coordinator decides when communication with an agent is required. In this paper, we formulate a general querying decision framework as an optimization problem that balances reducing the communication cost and decreasing the prediction error. Under this framework, we propose a joint query strategy that takes into account the joint statistics of the query and ADMM variables and the total communication cost of all agents in the presence of uncertainty caused by the GP regression. In addition, we derive three different decision mechanisms that simplify the general framework by making the communication decision for each agent individually. We integrate multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution’s accuracy/optimality. The proposed methods can achieve significant communication reduction and good optimization solution accuracy for distributed optimization, as demonstrated by extensive simulations of a distributed sharing problem.

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents' local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, due to data quantization, the GP algorithm is modified to account for the introduced quantization noise statistics. We further improve our approach by introducing an orthogonalization process to the quantizer's input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer's introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution's accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents.

Abstract Diabetes can cause microvessel impairment. However, these conjunctival pathological changes are not easily recognized, limiting their potential as independent diagnostic indicators. Therefore, we designed a deep learning model to explore the relationship between conjunctival features and diabetes, and to advance automated identification of diabetes through conjunctival images. Images were collected from patients with type 2 diabetes and healthy volunteers. A hierarchical multi-tasking network model (HMT-Net) was developed using conjunctival images, and the model was systematically evaluated and compared with other algorithms. The sensitivity, specificity, and accuracy of the HMT-Net model to identify diabetes were 78.70%, 69.08%, and 75.15%, respectively. The performance of the HMT-Net model was significantly better than that of ophthalmologists. The model allowed sensitive and rapid discrimination by assessment of conjunctival images and can be potentially useful for identifying diabetes.