# Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

arXiv: https://arxiv.org/abs/2108.03980 · HTML: https://mlsanity.com/paper/2108.03980/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 66 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [Blind Backdoors in Deep Learning Models](https://mlsanity.com/paper/2005.03823/) (2020)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Split Learning for collaborative deep learning in healthcare](https://mlsanity.com/paper/1912.12115/) (2019)
- [Model-Reuse Attacks on Deep Learning Systems](https://mlsanity.com/paper/1812.00483/) (2018)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Joint Service Pricing and Cooperative Relay Communication for Federated Learning](https://mlsanity.com/paper/1811.12082/) (2018)

## Built on by (most cited first, 15 of 15)

- [Feature Distribution Matching for Federated Domain Generalization](https://mlsanity.com/paper/2203.11635/) (2022)
- [Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis](https://mlsanity.com/paper/2203.11633/) (2022)
- [A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design](https://mlsanity.com/paper/2607.09084/) (2026)
- [Bidirectional Contrastive Split Learning for Visual Question Answering](https://mlsanity.com/paper/2208.11435/) (2022)
- [Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space](https://mlsanity.com/paper/2304.00436/) (2023)
- [DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models](https://mlsanity.com/paper/2404.08079/) (2024)
- [Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers](https://mlsanity.com/paper/2511.22616/) (2025)
- [Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection](https://mlsanity.com/paper/2110.06025/) (2021)
- [Mitigating Cross-client GANs-based Attack in Federated Learning](https://mlsanity.com/paper/2307.13314/) (2023)
- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [Learning to Help in Multi-Class Settings](https://mlsanity.com/paper/2501.13810/) (2025)
- [Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2505.09106/) (2025)
- [Meta Learning in Decentralized Neural Networks: Towards More General AI](https://mlsanity.com/paper/2302.01020/) (2023)
- [Meta Neural Coordination](https://mlsanity.com/paper/2305.12109/) (2023)
- [Event-Triggered Gossip for Distributed Learning](https://mlsanity.com/paper/2602.19116/) (2026)

## Abstract (arXiv)

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing of multi-layer neural networks within the networking of local clients, whereas, without disclosing the original local training data. Notably, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey paper, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.