# DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information

arXiv: https://arxiv.org/abs/2505.05842 · HTML: https://mlsanity.com/paper/2505.05842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach](https://mlsanity.com/paper/2001.04756/) (2020)
- [LoMar: A Local Defense Against Poisoning Attack on Federated Learning](https://mlsanity.com/paper/2201.02873/) (2022)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning](https://mlsanity.com/paper/2202.10938/) (2022)
- [Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective](https://mlsanity.com/paper/2207.12030/) (2022)
- [Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling](https://mlsanity.com/paper/2302.00106/) (2023)
- [Joint Participation Incentive and Network Pricing Design for Federated Learning](https://mlsanity.com/paper/2309.16712/) (2023)
- [Enhancing Federated Learning with spectrum allocation optimization and device selection](https://mlsanity.com/paper/2212.13544/) (2022)

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

- [Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation](https://mlsanity.com/paper/2605.18020/) (2026)

## Abstract (arXiv)

Online Federated Learning (OFL) is a real-time learning paradigm that sequentially executes parameter aggregation immediately for each random arriving client. To motivate clients to participate in OFL, it is crucial to offer appropriate incentives to offset the training resource consumption. However, the design of incentive mechanisms in OFL is constrained by the dynamic variability of Two-sided Incomplete Information (TII) concerning resources, where the server is unaware of the clients' dynamically changing computational resources, while clients lack knowledge of the real-time communication resources allocated by the server. To incentivize clients to participate in training by offering dynamic rewards to each arriving client, we design a novel Dynamic Bayesian persuasion pricing for online Federated learning (DaringFed) under TII. Specifically, we begin by formulating the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, and then demonstrate the existence of a unique Bayesian persuasion Nash equilibrium. By deriving the optimal design of DaringFed under one-sided incomplete information, we further analyze the approximate optimal design of DaringFed with a specific bound under TII. Finally, extensive evaluation conducted on real datasets demonstrate that DaringFed optimizes accuracy and converges speed by 16.99%, while experiments with synthetic datasets validate the convergence of estimate unknown values and the effectiveness of DaringFed in improving the server's utility by up to 12.6%.