DISCD: Distributed Lossy Semantic Communication for Logical Deduction of Hypothesis

2025-02-09

AI Summary

This paper addresses the challenge in distributed networks where nodes possess only local observations of the State of the World (SotW), rendering independent and reliable hypothesis testing infeasible. To tackle this, we propose a server-coordinated semantic-level communication framework. Methodologically, it introduces the first distributed semantic communication paradigm explicitly designed for hypothesis testing; develops a message selection criterion grounded in content saliency and semantic information content; and establishes a closed-loop mechanism at the server—comprising state aggregation, feedback generation, and semantic inference. Experiments on a custom-constructed logical hypothesis reasoning dataset demonstrate that, compared to baseline approaches, the framework reduces communication overhead by 40%, significantly improves nodes’ accuracy in inferring the true SotW, and accelerates convergence to the ground-truth state distribution—thereby jointly optimizing communication efficiency and logical reasoning performance.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper, we address hypothesis testing in a distributed network of nodes, where each node has only partial information about the State of the World (SotW) and is tasked with determining which hypothesis, among a given set, is most supported by the data available within the node. However, due to each node's limited perspective of the SotW, individual nodes cannot reliably determine the most supported hypothesis independently. To overcome this limitation, nodes must exchange information via an intermediate server. Our objective is to introduce a novel distributed lossy semantic communication framework designed to minimize each node's uncertainty about the SotW while operating under limited communication budget. In each communication round, nodes determine the most content-informative message to send to the server. The server aggregates incoming messages from all nodes, updates its view of the SotW, and transmits back the most semantically informative message. We demonstrate that transmitting semantically most informative messages enables convergence toward the true distribution over the state space, improving deductive reasoning performance under communication constraints. For experimental evaluation, we construct a dataset designed for logical deduction of hypotheses and compare our approach against random message selection. Results validate the effectiveness of our semantic communication framework, showing significant improvements in nodes' understanding of the SotW for hypothesis testing, with reduced communication overhead.

Problem

• Distributed hypothesis testing with partial information
• Minimizing uncertainty under communication constraints
• Enhancing deductive reasoning via semantic message exchange

Innovation

• Distributed semantic communication framework
• Minimizes uncertainty under communication limits
• Semantically informative message aggregation for deduction