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Blockchain-based Framework for Scalable and Incentivized Federated Learning

Bijun Wu, Oshani Seneviratne

TL;DR

This paper tackles trust, scalability, and fairness in federated learning by introducing a blockchain-based framework that uses smart contracts to automate registration, update validation, reward distribution, and global state maintenance. Its core contribution is a hybrid incentive mechanism that combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure high-quality and sustained client participation, while offloading heavy computations off-chain to control gas costs. The architecture leverages a private Ethereum network and IPFS to balance transparency with efficiency, and it demonstrates feasibility through gas-cost analyses across varying model sizes, highlighting the trade-offs and potential optimizations needed for resource-intensive deployments like LLMs. Overall, the work provides a practical blueprint for decentralized, fair, and scalable FL with measurable economic considerations, and it offers concrete directions for future benchmarking and optimizations in real-world ecosystems.

Abstract

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mechanisms, introducing trust issues, single points of failure, and limited mechanisms for incentivizing meaningful client contributions. These challenges are exacerbated as FL scales to train resource-intensive models, such as large language models (LLMs), requiring scalable, decentralized solutions. This paper presents a blockchain-based FL framework that addresses these limitations by integrating smart contracts and a novel hybrid incentive mechanism. The framework automates critical FL tasks, including client registration, update validation, reward distribution, and maintaining a transparent global state. The hybrid incentive mechanism combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure fairness, transparency, and sustained engagement. We evaluate the framework through gas cost analysis, demonstrating its feasibility for different scales of federated learning scenarios.

Blockchain-based Framework for Scalable and Incentivized Federated Learning

TL;DR

This paper tackles trust, scalability, and fairness in federated learning by introducing a blockchain-based framework that uses smart contracts to automate registration, update validation, reward distribution, and global state maintenance. Its core contribution is a hybrid incentive mechanism that combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure high-quality and sustained client participation, while offloading heavy computations off-chain to control gas costs. The architecture leverages a private Ethereum network and IPFS to balance transparency with efficiency, and it demonstrates feasibility through gas-cost analyses across varying model sizes, highlighting the trade-offs and potential optimizations needed for resource-intensive deployments like LLMs. Overall, the work provides a practical blueprint for decentralized, fair, and scalable FL with measurable economic considerations, and it offers concrete directions for future benchmarking and optimizations in real-world ecosystems.

Abstract

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mechanisms, introducing trust issues, single points of failure, and limited mechanisms for incentivizing meaningful client contributions. These challenges are exacerbated as FL scales to train resource-intensive models, such as large language models (LLMs), requiring scalable, decentralized solutions. This paper presents a blockchain-based FL framework that addresses these limitations by integrating smart contracts and a novel hybrid incentive mechanism. The framework automates critical FL tasks, including client registration, update validation, reward distribution, and maintaining a transparent global state. The hybrid incentive mechanism combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure fairness, transparency, and sustained engagement. We evaluate the framework through gas cost analysis, demonstrating its feasibility for different scales of federated learning scenarios.

Paper Structure

This paper contains 19 sections, 6 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Architecture
  4. Hybrid Incentive Mechanism Design
  5. Alignment-Based Rewards
  6. Periodic Fairness Checks via Cumulative Scoring
  7. Consistency Multipliers
  8. Evaluation
  9. Gas Costs and Simulation Setup
  10. Baseline Deployment Metrics
  11. Gas Consumption Analysis
  12. Discussion
  13. Limitations
  14. Challenges and Mitigation Strategies
  15. Scalability
  16. ...and 4 more sections

Figures (3)

  • Figure 1: System Architecture.
  • Figure 2: Allocation of Rewards.
  • Figure 3: Sequence Diagram for Hybrid Incentive Mechanism Design.