# Online Distributed Learning with Quantized Finite-Time Coordination

arXiv: https://arxiv.org/abs/2307.06620 · HTML: https://mlsanity.com/paper/2307.06620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning](https://mlsanity.com/paper/2110.09993/) (2021)
- [Time-Varying Convex Optimization: Time-Structured Algorithms and Applications](https://mlsanity.com/paper/2006.08500/) (2020)
- [3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation](https://mlsanity.com/paper/2202.00998/) (2022)
- [Balancing Communication and Computation in Distributed Optimization](https://mlsanity.com/paper/1709.02999/) (2017)
- [Online Learning with Inexact Proximal Online Gradient Descent Algorithms](https://mlsanity.com/paper/1806.00202/) (2018)
- [Optimization and Learning with Information Streams: Time-varying Algorithms and Applications](https://mlsanity.com/paper/1910.08123/) (2019)
- [ADD-OPT: Accelerated Distributed Directed Optimization](https://mlsanity.com/paper/1607.04757/) (2016)
- [Can Primal Methods Outperform Primal-dual Methods in Decentralized Dynamic Optimization?](https://mlsanity.com/paper/2003.00816/) (2020)
- [Distributed Optimization for Smart Cyber-Physical Networks](https://mlsanity.com/paper/1906.10760/) (2019)
- [A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks](https://mlsanity.com/paper/2003.13980/) (2020)
- [Distributed and Inexact Proximal Gradient Method for Online Convex Optimization](https://mlsanity.com/paper/2001.00870/) (2020)
- [Exact worst-case convergence rates of the proximal gradient method for composite convex minimization](https://mlsanity.com/paper/1705.04398/) (2017)
- [Distributed Optimization with Quantized Gradient Descent](https://mlsanity.com/paper/2211.10855/) (2022)

## Built on by (most cited first, 1 of 1)

- [Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers](https://mlsanity.com/paper/2510.25176/) (2025)

## Abstract (arXiv)

In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task.