Reward-Based 1-bit Compressed Federated Distillation on Blockchain

Abstract

The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes. This security-per-design approach in combination with increasingly performant Internet of Things (IoT) and mobile devices opens up a new realm of possibilities to utilize private data from industries as well as from individuals as input for artificial intelligence model training. Yet in previous FL systems, lack of trust due to the imbalance of power between workers and a central authority, the assumption of altruistic worker participation and the inability to correctly measure and compare contributions of workers hinder this technology from scaling beyond small groups of already entrusted entities towards mass adoption. This work aims to mitigate the aforementioned issues by introducing a novel decentralized federated learning framework where heavily compressed 1-bit soft-labels, resembling 1-hot label predictions, are aggregated on a smart contract. In a context where workers' contributions are now easily comparable, we modify the Peer Truth Serum for Crowdsourcing mechanism (PTSC) for FD to reward honest participation based on peer consistency in an incentive compatible fashion. Due to heavy reductions of both computational complexity and storage, our framework is a fully on-blockchain FL system that is feasible on simple smart contracts and therefore blockchain agnostic. We experimentally test our new framework and validate its theoretical properties.

References in corpus (16)

• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Learning for Mobile Keyboard Prediction
• Ternary Weight Networks
• A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
• Ensemble Distillation for Robust Model Fusion in Federated Learning
• EMNIST: an extension of MNIST to handwritten letters
• Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
• Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data
• FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC
• Crowdsourcing with Tullock contests: A new perspective
• GFL: A Decentralized Federated Learning Framework Based On Blockchain
• FedCoin: A Peer-to-Peer Payment System for Federated Learning
• Incentives for Federated Learning: a Hypothesis Elicitation Approach
• Communication-Efficient Federated Distillation
• Ternary Compression for Communication-Efficient Federated Learning
• A closer look at the approximation capabilities of neural networks