Project Details
Blockchain-Based Federated Learning with Adaptive Differential Privacy 2025
Project Overview
This project implements a Blockchain-based Federated Learning (BC-FL) system with Adaptive Differential Privacy that eliminates the need for a trusted central aggregator. The system combines Ethereum smart contracts for decentralized coordination, IPFS for distributed model storage, and adaptive differential privacy to optimize the privacy-utility trade-off.
The key innovation is replacing the traditional central FL server with a blockchain-based coordination protocol, while implementing an adaptive noise mechanism that dynamically adjusts privacy parameters based on training progress, optimizing both model utility and privacy budget consumption.
System Architecture
Three-Layer Architecture
Compute Layer (Client-Side):
Local model training with PyTorch, adaptive noise injection using Opacus, gradient clipping and privacy accounting, IPFS upload/download operationsStorage Layer (Off-Chain):
IPFS for decentralized model storage, content-addressed storage (CID-based), tamper-proof model versioningCoordination Layer (On-Chain):
FLRegistry.sol smart contract (Ethereum/Ganache), round management and state tracking, privacy budget enforcement, client reputation system
Key Features
Privacy Mechanisms
- Centralized DP (CDP)
- Local DP (LDP)
- Adaptive DP
- Adaptive Clipping
Blockchain Features
- Client registration
- Reputation tracking
- Privacy budget enforcement
- IPFS hash verification
Technical Details
Model Architecture
Dataset: CIFAR-10 (32×32 RGB images, 10 classes). Lightweight CNN with 2 convolutional layers, 2 fully connected layers, and GroupNorm for DP compliance.
Privacy Accounting
Custom Rényi Differential Privacy (RDP) accountant that tracks privacy budget across multiple rounds with varying noise levels, converting RDP to (ε, δ)-DP using optimal alpha selection.
Adaptive Mechanism
Monitors validation loss after each epoch and dynamically adjusts noise multiplier based on loss trend. Loss improving → reduce noise (spend budget), Loss stagnating → increase noise (conserve budget).
Project Information
EECE 571B - Blockchain Foundations
CIFAR-10
Python, PyTorch, Solidity, Ethereum, IPFS, Opacus, Foundry, Ganache
Federated Learning, Differential Privacy, Blockchain, Smart Contracts, Distributed Storage
Key Results
Privacy-Utility Trade-off
Adaptive DP achieves better accuracy for similar privacy budgets compared to static approaches.
Decentralization Cost
Local DP (decentralized) has lower utility than Centralized DP but eliminates trust requirements.
Economic Feasibility
Mainnet costs are prohibitive (~$17/round), but Layer 2 solutions reduce costs by 95%.