# Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning

arXiv: https://arxiv.org/abs/2410.19321 · HTML: https://mlsanity.com/paper/2410.19321/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 61 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (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)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Learning the Pareto Front with Hypernetworks](https://mlsanity.com/paper/2010.04104/) (2020)
- [FedALA: Adaptive Local Aggregation for Personalized Federated Learning](https://mlsanity.com/paper/2212.01197/) (2022)
- [FedDisco: Federated Learning with Discrepancy-Aware Collaboration](https://mlsanity.com/paper/2305.19229/) (2023)
- [Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation](https://mlsanity.com/paper/2010.00753/) (2020)
- [Mechanisms that Incentivize Data Sharing in Federated Learning](https://mlsanity.com/paper/2207.04557/) (2022)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Byzantine-resilient Decentralized Stochastic Gradient Descent](https://mlsanity.com/paper/2002.08569/) (2020)
- [Learning Conjoint Attentions for Graph Neural Nets](https://mlsanity.com/paper/2102.03147/) (2021)
- [Fairness in Federated Learning via Core-Stability](https://mlsanity.com/paper/2211.02091/) (2022)
- [Industry-Scale Orchestrated Federated Learning for Drug Discovery](https://mlsanity.com/paper/2210.08871/) (2022)
- [Collaboration Equilibrium in Federated Learning](https://mlsanity.com/paper/2108.07926/) (2021)
- [MarS-FL: Enabling Competitors to Collaborate in Federated Learning](https://mlsanity.com/paper/2110.13464/) (2021)
- [Towards the Practical Utility of Federated Learning in the Medical Domain](https://mlsanity.com/paper/2207.03075/) (2022)
- [Coopetition Against an Amazon](https://mlsanity.com/paper/2005.10038/) (2020)
- [Strategic Data Sharing between Competitors](https://mlsanity.com/paper/2305.16052/) (2023)
- [FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants](https://mlsanity.com/paper/2312.11391/) (2023)
- [Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning](https://mlsanity.com/paper/2410.07286/) (2024)
- [Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance](https://mlsanity.com/paper/2007.15314/) (2020)
- [A Framework for Allocating Server Time to Spot and On-demand Services in Cloud Computing](https://mlsanity.com/paper/1902.01321/) (2019)

## Built on by (most cited first, 9 of 9)

- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [Personalized Federated Learning via Learning Dynamic Graphs](https://mlsanity.com/paper/2503.05474/) (2025)
- [A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning](https://mlsanity.com/paper/2509.18120/) (2025)
- [Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning](https://mlsanity.com/paper/2505.20648/) (2025)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)
- [Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning](https://mlsanity.com/paper/2512.24959/) (2025)
- [Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning](https://mlsanity.com/paper/2604.14886/) (2026)
- [Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach](https://mlsanity.com/paper/2607.26788/) (2026)
- [FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting](https://mlsanity.com/paper/2608.15310/) (2026)

## Abstract (arXiv)

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs. This requires the desirable FL-PT selection strategy to simultaneously mitigate the problems of free riders and conflicts of interest among competitors. To this end, we propose an optimal FL collaboration formation strategy -- FedEgoists -- which ensures that: (1) a FL-PT can benefit from FL if and only if it benefits the FL ecosystem, and (2) a FL-PT will not contribute to its competitors or their supporters. It provides an efficient clustering solution to group FL-PTs into coalitions, ensuring that within each coalition, FL-PTs share the same interest. We theoretically prove that the FL-PT coalitions formed are optimal since no coalitions can collaborate together to improve the utility of any of their members. Extensive experiments on widely adopted benchmark datasets demonstrate the effectiveness of FedEgoists compared to nine state-of-the-art baseline methods, and its ability to establish efficient collaborative networks in cross-silos FL with FL-PTs that engage in business activities.