# Central Server Free Federated Learning over Single-sided Trust Social Networks

arXiv: https://arxiv.org/abs/1910.04956 · HTML: https://mlsanity.com/paper/1910.04956/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 30 of 33)

- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://mlsanity.com/paper/2104.08815/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [On the (In)security of Peer-to-Peer Decentralized Machine Learning](https://mlsanity.com/paper/2205.08443/) (2022)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection](https://mlsanity.com/paper/2106.07976/) (2021)
- [Distributed Learning Systems with First-order Methods](https://mlsanity.com/paper/2104.05245/) (2021)
- [FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning](https://mlsanity.com/paper/2403.16561/) (2024)
- [Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks](https://mlsanity.com/paper/2204.03027/) (2022)
- [FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2104.07145/) (2021)
- [Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent](https://mlsanity.com/paper/2104.08100/) (2021)
- [Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://mlsanity.com/paper/2303.00524/) (2023)
- [Zone-based Federated Learning for Mobile Sensing Data](https://mlsanity.com/paper/2303.06246/) (2023)
- [Multi-Tier Client Selection for Mobile Federated Learning Networks](https://mlsanity.com/paper/2305.06865/) (2023)
- [Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology](https://mlsanity.com/paper/2207.09657/) (2022)
- [Over-The-Air Clustered Wireless Federated Learning](https://mlsanity.com/paper/2211.03363/) (2022)
- [Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks](https://mlsanity.com/paper/2307.09977/) (2023)
- [Trust Driven On-Demand Scheme for Client Deployment in Federated Learning](https://mlsanity.com/paper/2405.00395/) (2024)
- [A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods](https://mlsanity.com/paper/2406.02273/) (2024)
- [Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction](https://mlsanity.com/paper/2412.03188/) (2024)
- [Sharp Gaussian approximations for Decentralized Federated Learning](https://mlsanity.com/paper/2505.08125/) (2025)
- [The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset](https://mlsanity.com/paper/2108.03508/) (2021)
- [Federated Learning on Stochastic Neural Networks](https://mlsanity.com/paper/2506.08169/) (2025)

## Abstract (arXiv)

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows very interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.