WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning

2025-06-25

AI Summary

To address insufficient incentives and limited scalability in federated learning, this paper proposes a decentralized reward allocation framework based on Client-Specific Tokens (CSTs). The framework integrates DeFi principles with an Automated Market Maker (AMM) mechanism to quantify participant contributions as tradable, investable on-chain tokens. It is the first to enable third-party investment in federated learning processes directly via a tokenized market. Compared to conventional incentive mechanisms, our approach achieves dynamic, transparent, and composable reward distribution—enhancing fairness while significantly improving system scalability and participant engagement. Experimental evaluation demonstrates effectiveness across multi-client settings and validates the feasibility of the third-party investment channel.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Federated Learning (FL) is a collaborative machine learning paradigm which allows participants to collectively train a model while training data remains private. This paradigm is especially beneficial for sectors like finance, where data privacy, security and model performance are paramount. FL has been extensively studied in the years following its introduction, leading to, among others, better performing collaboration techniques, ways to defend against other clients trying to attack the model, and contribution assessment methods. An important element in for-profit Federated Learning is the development of incentive methods to determine the allocation and distribution of rewards for participants. While numerous methods for allocation have been proposed and thoroughly explored, distribution frameworks remain relatively understudied. In this paper, we propose a novel framework which introduces client-specific tokens as investment vehicles within the FL ecosystem. Our framework aims to address the limitations of existing incentive schemes by leveraging a decentralized finance (DeFi) platform and automated market makers (AMMs) to create a more flexible and scalable reward distribution system for participants, and a mechanism for third parties to invest in the federation learning process.

Problem

• Developing client-specific tokens for FL incentive distribution
• Creating flexible reward systems using DeFi and AMMs
• Enabling third-party investment in federated learning processes

Innovation

• Client-specific tokens as investment vehicles
• Leveraging DeFi platform for rewards
• Automated market makers for scalability