Distributed Functional Compression for Independent Component Analysis in Wireless Networks

In this paper, we consider distributed Independent Component Analysis (ICA) in wireless networks, where data from several geographically distributed wireless nodes (nodes) must be transmitted to a central server (server) to extract original sources through ICA. However, transmitting the vast amount of data over wireless channels to the server poses significant challenges due to limited bandwidth and privacy concerns. Our research addresses how to encode node data to meet channel rate constraints while providing maximally relevant information for ICA. Particularly, we propose a distributed functional compression framework for learning ICA over orthogonal AWGN channels. The framework leverages the Information Bottleneck (IB) principle to encode and compress the data to meet the channel rate constraint while maximally preserving the functionally relevant information for ICA. We train both neural encoders at the nodes and a neural decoder at the server in an unsupervised manner using the IB principle. We consider ICA for both linear and nonlinear mixing setups. Compared to the state-of-the-art, over real dataset, our proposed framework demonstrates a remarkable improvement of up to approximately 43% in accurately estimating the source signals in ICA while meeting the channels’ rate constraints. Finally, we propose a three-stage training algorithm, where the raw sensory data never leaves the nodes either for training or inference, to reduce the communication overhead. We show that our proposed training algorithm notably reduces channel use compared to the traditional cloud-based method, where the observed data from the nodes are compressed and transmitted to the cloud for learning ICA.