Fully Decentralized and Federated Low Rank Compressive Sensing

TL;DR

This paper introduces a decentralized federated approach for low-rank matrix recovery in compressive sensing, enabling privacy-preserving image reconstruction across distributed devices without central coordination.

Contribution

It proposes a novel decentralized projected gradient descent algorithm for low-rank matrix recovery in federated settings, ensuring data privacy and local reconstruction.

Findings

Effective reconstruction demonstrated through extensive simulations.

Preserves user privacy by sharing only summaries, not raw data.

Achieves consensus-based global estimation without a central node.

Abstract

In this work we develop a fully decentralized, federated, and fast solution to the recently studied Low Rank Compressive Sensing (LRCS) problem: recover an nxq low-rank matrix from column-wise linear projections. An important application where this problem occurs, and a decentralized solution is desirable is in federated sketching: efficiently compressing the vast amounts of distributed images/videos generated by smartphones and various other devices while respecting the users' privacy. Images from different devices, once grouped by category, are similar and hence the matrix formed by the vectorized images of a certain category is well-modeled as being low rank. Suppose there are p nodes (say p smartphones), and each store a subset of the sketches of its images. We develop a decentralized projected gradient descent (GD) based approach to jointly reconstruct the images of all the phones/users from their respective stored sketches. The algorithm is such that the phones/users never share their raw data but only summaries of this data with the other phones at each algorithm iteration. Also, the reconstructed images of user g are obtained only locally. Other users cannot reconstruct them. Only the column span of the matrix is reconstructed globally. By "decentralized" we mean that there is no central node to which all nodes are connected and thus the only way to aggregate the summaries from the various nodes is by use of an iterative consensus algorithm that eventually provides an estimate of the aggregate at each node, as long as the network is strongly connected. We validated the effectiveness of our algorithm via extensive simulation experiments.