Fairness in Multiterminal Data Compression: A Splitting Method for The Egalitarian Solution

TL;DR

This paper introduces a fast, parallel algorithm for achieving fair data compression in multiterminal networks, optimizing energy use and reducing computation time compared to existing methods.

Contribution

A novel splitting algorithm (SPLIT) that efficiently finds the egalitarian solution in the Slepian-Wolf region with parallel and distributed computation capabilities.

Findings

Significant reduction in computation time with parallel implementation.

The egalitarian solution outperforms the Shapley value in balancing energy consumption.

The algorithm completes in strongly polynomial time.

Abstract

This paper proposes a novel splitting (SPLIT) algorithm to achieve fairness in the multiterminal lossless data compression problem. It finds the egalitarian solution in the Slepian-Wolf region and completes in strongly polynomial time. We show that the SPLIT algorithm adaptively updates the source coding rates to the optimal solution, while recursively splitting the terminal set, enabling parallel and distributed computation. The result of an experiment demonstrates a significant reduction in computation time by the parallel implementation when the number of terminals becomes large. The achieved egalitarian solution is also shown to be superior to the Shapley value in distributed networks, e.g., wireless sensor networks, in that it best balances the nodes' energy consumption and is far less computationally complex to obtain.