# DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game

arXiv: https://arxiv.org/abs/2412.15492 · HTML: https://mlsanity.com/paper/2412.15492/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [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)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing](https://mlsanity.com/paper/2211.15281/) (2022)
- [Oort: Efficient Federated Learning via Guided Participant Selection](https://mlsanity.com/paper/2010.06081/) (2020)

## Built on by (most cited first, 5 of 5)

- [GamiBench: Evaluating Spatial Reasoning and 2D-to-3D Planning Capabilities of MLLMs with Origami Folding Tasks](https://mlsanity.com/paper/2512.22207/) (2025)
- [ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning](https://mlsanity.com/paper/2602.08014/) (2026)
- [SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System](https://mlsanity.com/paper/2606.18384/) (2026)
- [Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach](https://mlsanity.com/paper/2607.26788/) (2026)
- [Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems](https://mlsanity.com/paper/2609.19695/) (2026)

## Abstract (arXiv)

Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility.