# Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph

arXiv: https://arxiv.org/abs/2210.00325 · HTML: https://mlsanity.com/paper/2210.00325/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 79 references that are in the map.
- First posted on arXiv 1 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [When the Curious Abandon Honesty: Federated Learning Is Not Private](https://mlsanity.com/paper/2112.02918/) (2021)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Peer-to-peer Federated Learning on Graphs](https://mlsanity.com/paper/1901.11173/) (2019)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [HDNET: Exploiting HD Maps for 3D Object Detection](https://mlsanity.com/paper/2012.11704/) (2020)
- [Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification](https://mlsanity.com/paper/2202.00580/) (2022)
- [Eluding Secure Aggregation in Federated Learning via Model Inconsistency](https://mlsanity.com/paper/2111.07380/) (2021)
- [Personalized and Private Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1705.08435/) (2017)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix](https://mlsanity.com/paper/2106.06089/) (2021)
- [POSEIDON: Privacy-Preserving Federated Neural Network Learning](https://mlsanity.com/paper/2009.00349/) (2020)
- [A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee](https://mlsanity.com/paper/2108.00365/) (2021)
- [Privacy-preserving Decentralized Aggregation for Federated Learning](https://mlsanity.com/paper/2012.07183/) (2020)
- [Tutorial on dynamic average consensus: the problem, its applications, and the algorithms](https://mlsanity.com/paper/1803.04628/) (2018)
- [Scalable Privacy-Preserving Distributed Learning](https://mlsanity.com/paper/2005.09532/) (2020)
- [Secure and Privacy-Preserving Consensus](https://mlsanity.com/paper/1707.04491/) (2017)
- [Privacy-Preserving Average Consensus via State Decomposition](https://mlsanity.com/paper/1902.09576/) (2019)
- [Privacy preserving distributed optimization using homomorphic encryption](https://mlsanity.com/paper/1805.00572/) (2018)
- [Differentially Private Average Consensus: Obstructions, Trade-Offs, and Optimal Algorithm Design](https://mlsanity.com/paper/1512.09039/) (2015)

## Built on by (most cited first, 11 of 11)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation](https://mlsanity.com/paper/2410.19265/) (2024)
- [Noiseless Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2404.09536/) (2024)
- [On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning](https://mlsanity.com/paper/2308.14606/) (2023)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [SCoTTi: Save Computation at Training Time with an adaptive framework](https://mlsanity.com/paper/2312.12483/) (2023)
- [Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks](https://mlsanity.com/paper/2409.17189/) (2024)
- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [Fair Decentralized Learning](https://mlsanity.com/paper/2410.02541/) (2024)
- [ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning](https://mlsanity.com/paper/2506.15181/) (2025)
- [Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities](https://mlsanity.com/paper/2607.03171/) (2026)

## Abstract (arXiv)

Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an environment of high mobility, where the communication graph between the learners may vary between successive rounds of model aggregation. In particular, in each round of global model aggregation, the Metropolis-Hastings method is applied to update the weighted adjacency matrix based on the current communication topology. In addition, the Shamir's secret sharing scheme is integrated to facilitate privacy in reaching consensus of the global model. The paper establishes the correctness and privacy properties of the proposed algorithm. The computational efficiency is evaluated by a simulation built on a federated learning framework with a real-word dataset.