FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism

12/19/2020
by   Jiasi Weng, et al.
0

Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacking data and models, a convenient approach is needed which enables the trained models from various sources for prediction serving, but it has yet to truly take off considering three issues: (i) incentivizing prediction truthfulness; (ii) boosting prediction accuracy; (iii) protecting model privacy. We design FedServing, a federated prediction serving framework, achieving the three issues. First, we customize an incentive mechanism based on Bayesian game theory which ensures that joining providers at a Bayesian Nash Equilibrium will provide truthful (not meaningless) predictions. Second, working jointly with the incentive mechanism, we employ truth discovery algorithms to aggregate truthful but possibly inaccurate predictions for boosting prediction accuracy. Third, providers can locally deploy their models and their predictions are securely aggregated inside TEEs. Attractively, our design supports popular prediction formats, including top-1 label, ranked labels and posterior probability. Besides, blockchain is employed as a complementary component to enforce exchange fairness. By conducting extensive experiments, we validate the expected properties of our design. We also empirically demonstrate that FedServing reduces the risk of certain membership inference attack.

READ FULL TEXT
02/22/2020

FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

Promising federated learning coupled with Mobile Edge Computing (MEC) is...
09/11/2020

Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning

Federated Learning rests on the notion of training a global model distri...
06/27/2021

A Comprehensive Survey of Incentive Mechanism for Federated Learning

Federated learning utilizes various resources provided by participants t...
07/25/2022

Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective

Federated learning (FL) is a promising distributed framework for collabo...
01/24/2021

Incentive Mechanism Design for Federated Learning: Hedonic Game Approach

Incentive mechanism design is crucial for enabling federated learning. W...
06/14/2020

Game of Duplicity: A Proactive Automated Defense Mechanism by Deception Design

We present a new game framework called the duplicity game to design defe...
06/27/2021

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

The recent advent of various forms of Federated Knowledge Distillation (...