# Communication-efficient Distributed Multi-resource Allocation

arXiv: https://arxiv.org/abs/1807.10860 · HTML: https://mlsanity.com/paper/1807.10860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Multi-resource allocation for federated settings: A non-homogeneous Markov chain model](https://mlsanity.com/paper/2104.12828/) (2021)
- [Near-optimal Differentially Private Client Selection in Federated Settings](https://mlsanity.com/paper/2310.09370/) (2023)
- [Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry](https://mlsanity.com/paper/2604.00319/) (2026)

## Abstract (arXiv)

In several smart city applications, multiple resources must be allocated among competing agents that are coupled through such shared resources and are constrained --- either through limitations of communication infrastructure or privacy considerations. We propose a distributed algorithm to solve such distributed multi-resource allocation problems with no direct inter-agent communication. We do so by extending a recently introduced additive-increase multiplicative-decrease (AIMD) algorithm, which only uses very little communication between the system and agents. Namely, a control unit broadcasts a one-bit signal to agents whenever one of the allocated resources exceeds capacity. Agents then respond to this signal in a probabilistic manner. In the proposed algorithm, each agent makes decision of its resource demand locally and an agent is unaware of the resource allocation of other agents. In empirical results, we observe that the average allocations converge over time to optimal allocations.