As federated learning and federalized learning technologies continue to evolve, enabling secure and efficient data processing across distributed entities has become an increasingly important research focus. One of the core challenges in these approaches is how to fairly allocate the profits generated by data collaborations, especially when different entities contribute distinct, and potentially unequal, amounts of valuable data.

　　"Federated Learning Meets Federalized Learning: Enabling Scalable and Fair Profit Sharing for Horizontal Data Federation" by Keren Ye, Shangqing Wu, Yiwei Zhang, Qingsong Wen, Yuexuan Wang, and Bo Hu, recently published in Scientific Reports, delves into this critical issue. The study aims to address the problem of profit division among participants in a horizontal data federation setup facilitated by federated and federalized learning.

Introduction to Federated and Federalized Learning

　　Federated learning (FL) and federalized learning (FL) are decentralized machine learning techniques where data remains with local devices or organizations, safeguarding privacy and reducing the need for centralized data storage. In horizontal data federation, multiple organizations contribute their data to jointly train a global model without sharing the raw datasets, ensuring data privacy and compliance with regulations such as the GDPR.

Challenges in Profit Sharing

　　When entities collaborate on data to jointly train models for monetary gains, disputes may arise over how rewards should be distributed. This situation can lead to conflicts over fairness and equal contribution, especially when the data provided by each party differs significantly in volume and quality.

Proposed Solution

　　The authors propose a scalable and fair profit-sharing mechanism tailored to horizontal data federation. Their solution considers both the quantity of data each entity contributes and the uniqueness or value of their data. They introduce a method to assess the quality of data from different sources and the contribution it provides when training models.

Enabling Profit Sharing

　　The mechanism leverages the concept of "profit potential," which quantifies an entity’s contribution to the overall value of the model. This metric takes into account not just the volume of data but also factors such as diversity and accuracy. By measuring profit potential, the authors argue that entities can agree on a profit-sharing scheme that reflects the actual value they bring to the collaboration.

Comparative Analysis

　　The study compares different methods of profit sharing and evaluates their fairness and efficiency through both simulation and theoretical analysis. They demonstrate that their 'profit potential' approach leads to a fairer distribution of profits compared to more simplistic models, such as equal sharing or proportional sharing based solely on data volume.

Case Study and Evaluation

　　The authors present a case study involving multiple entities with varying data characteristics to showcase the effectiveness of their proposed method. The study shows how the profit-sharing framework can be applied in a real-world scenario, facilitating fair outcomes that encourage continued collaboration and data sharing.

Significance and Future Work

　　The paper concludes with a discussion of the significance of fair profit sharing in advancing the adoption of federated learning and federalized learning. The authors highlight the importance of ensuring that all parties receive equitable benefits, which can lead to sustained participation in data federations. Future work may extend this framework to different types of data federations and explore more nuanced aspects of value assessment in data collaborations.

Conclusion

　　"Federated Learning Meets Federalized Learning: Enabling Scalable and Fair Profit Sharing for Horizontal Data Federation" offers a crucial contribution to the field, addressing the pressing need for equitable profit distributions in data-sharing collaborations. As federated and federalized learning technologies continue to gain traction across industries, approaches like this will play a pivotal role in ensuring that these systems function efficiently and ethically.