# Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2205.07855 · HTML: https://mlsanity.com/paper/2205.07855/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 120 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 30 of 33)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://mlsanity.com/paper/2104.08815/) (2021)
- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018)
- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020)
- [BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture](https://mlsanity.com/paper/2010.07427/) (2020)
- [Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1811.09904/) (2018)
- [Transparent Contribution Evaluation for Secure Federated Learning on Blockchain](https://mlsanity.com/paper/2101.10572/) (2021)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Estimation of Individual Device Contributions for Incentivizing Federated Learning](https://mlsanity.com/paper/2009.09371/) (2020)
- [Incentives for Federated Learning: a Hypothesis Elicitation Approach](https://mlsanity.com/paper/2007.10596/) (2020)
- [Data-Free Evaluation of User Contributions in Federated Learning](https://mlsanity.com/paper/2108.10623/) (2021)

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

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [Smart Policy Control for Securing Federated Learning Management System](https://mlsanity.com/paper/2305.09134/) (2023)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing](https://mlsanity.com/paper/2302.13258/) (2023)
- [A Systematic Literature Review on Client Selection in Federated Learning](https://mlsanity.com/paper/2306.04862/) (2023)
- [A Framework for Incentivized Collaborative Learning](https://mlsanity.com/paper/2305.17052/) (2023)
- [Blockchain and Artificial Intelligence: Synergies and Conflicts](https://mlsanity.com/paper/2405.13462/) (2024)
- [Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning](https://mlsanity.com/paper/2407.04460/) (2024)
- [Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization](https://mlsanity.com/paper/2405.07415/) (2024)
- [Centralized vs Decentralized Federated Learning: A trade-off performance analysis](https://mlsanity.com/paper/2605.16089/) (2026)
- [Scalable Data Point Valuation in Decentralized Learning](https://mlsanity.com/paper/2305.01657/) (2023)
- [Strategic Incentivization for Locally Differentially Private Federated Learning](https://mlsanity.com/paper/2508.07138/) (2025)
- [Knowledge-Free Correlated Agreement for Incentivizing Federated Learning](https://mlsanity.com/paper/2605.04747/) (2026)

## Abstract (arXiv)

The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.