# Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP

arXiv: https://arxiv.org/abs/2211.09723 · HTML: https://mlsanity.com/paper/2211.09723/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Communication-Efficient Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2003.06307/) (2020)
- [Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training](https://mlsanity.com/paper/1805.07891/) (2018)
- [Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning](https://mlsanity.com/paper/2003.04544/) (2020)
- [Dynamic Stale Synchronous Parallel Distributed Training for Deep Learning](https://mlsanity.com/paper/1908.11848/) (2019)
- [Distilling On-Device Intelligence at the Network Edge](https://mlsanity.com/paper/1908.05895/) (2019)
- [Learning to Harness Bandwidth with Multipath Congestion Control and Scheduling](https://mlsanity.com/paper/2105.14271/) (2021)
- [Multipath TCP: Analysis, Design and Implementation](https://mlsanity.com/paper/1308.3119/) (2013)
- [On SDN-Enabled Online and Dynamic Bandwidth Allocation for Stream Analytics](https://mlsanity.com/paper/1811.04377/) (2018)
- [Communication optimization strategies for distributed deep neural network training: A survey](https://mlsanity.com/paper/2003.03009/) (2020)

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

- [Distilling Large Language Models for Network Active Queue Management](https://mlsanity.com/paper/2501.16734/) (2025)

## Abstract (arXiv)

The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL. The existing transmission control protocol (TCP)-based data networking schemes for DEL are application-agnostic and fail to deliver adjustments according to application layer requirements. As a result, they introduce massive excess time and undesired issues such as unfairness and stragglers. Other prior mitigation solutions have significant limitations as they balance data flow rates from workers across paths but often incur imbalanced backlogs when the paths exhibit variance, causing stragglers. To facilitate a more productive DEL, we develop a hybrid multipath TCP (MPTCP) by combining model-based and deep reinforcement learning (DRL) based MPTCP for DEL that strives to realize quicker iteration of DEL and better fairness (by ameliorating stragglers). Hybrid MPTCP essentially integrates two radical TCP developments: i) successful existing model-based MPTCP control strategies and ii) advanced emerging DRL-based techniques, and introduces a novel hybrid MPTCP data transport for easing the communication of the Agg-Avg process. Extensive emulation results demonstrate that the proposed hybrid MPTCP can overcome excess time consumption and ameliorate the application layer unfairness of DEL effectively without injecting additional inconstancy and stragglers.