Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Incentivizing Inclusive Contributions in Model Sharing Markets

Enpei Zhang, Jingyi Chai, Rui Ye, Yanfeng Wang, Siheng Chen

TL;DR

In response to data exhaustion and privacy concerns, the paper introduces iPFL, an inclusive, incentivized personalized federated learning framework that builds a graph-based model-sharing market coordinated by a neutral server. By coupling local losses with a model-similarity regularizer and maximizing social welfare, iPFL enables diverse participants to exchange models while preserving privacy; it provides theoretical guarantees of individual rationality and truthfulness and demonstrates superior economic utility across multiple tasks, including large-scale instruction-tuning, with competitive personalization performance. The approach includes an efficient graph-topology learning algorithm and a proximal-gradient training procedure, plus a reciprocal payment mechanism that discourages dishonest behavior and mitigates attacker impact. Overall, iPFL offers a practical pathway to leverage decentralized private data to train stronger AI while aligning incentives among heterogeneous stakeholders, as validated by extensive experiments and robust theoretical results.

Abstract

While data plays a crucial role in training contemporary AI models, it is acknowledged that valuable public data will be exhausted in a few years, directing the world's attention towards the massive decentralized private data. However, the privacy-sensitive nature of raw data and lack of incentive mechanism prevent these valuable data from being fully exploited. Addressing these challenges, this paper proposes inclusive and incentivized personalized federated learning (iPFL), which incentivizes data holders with diverse purposes to collaboratively train personalized models without revealing raw data. iPFL constructs a model-sharing market by solving a graph-based training optimization and incorporates an incentive mechanism based on game theory principles. Theoretical analysis shows that iPFL adheres to two key incentive properties: individual rationality and truthfulness. Empirical studies on eleven AI tasks (e.g., large language models' instruction-following tasks) demonstrate that iPFL consistently achieves the highest economic utility, and better or comparable model performance compared to baseline methods. We anticipate that our iPFL can serve as a valuable technique for boosting future AI models on decentralized private data while making everyone satisfied.

Incentivizing Inclusive Contributions in Model Sharing Markets

TL;DR

In response to data exhaustion and privacy concerns, the paper introduces iPFL, an inclusive, incentivized personalized federated learning framework that builds a graph-based model-sharing market coordinated by a neutral server. By coupling local losses with a model-similarity regularizer and maximizing social welfare, iPFL enables diverse participants to exchange models while preserving privacy; it provides theoretical guarantees of individual rationality and truthfulness and demonstrates superior economic utility across multiple tasks, including large-scale instruction-tuning, with competitive personalization performance. The approach includes an efficient graph-topology learning algorithm and a proximal-gradient training procedure, plus a reciprocal payment mechanism that discourages dishonest behavior and mitigates attacker impact. Overall, iPFL offers a practical pathway to leverage decentralized private data to train stronger AI while aligning incentives among heterogeneous stakeholders, as validated by extensive experiments and robust theoretical results.

Abstract

While data plays a crucial role in training contemporary AI models, it is acknowledged that valuable public data will be exhausted in a few years, directing the world's attention towards the massive decentralized private data. However, the privacy-sensitive nature of raw data and lack of incentive mechanism prevent these valuable data from being fully exploited. Addressing these challenges, this paper proposes inclusive and incentivized personalized federated learning (iPFL), which incentivizes data holders with diverse purposes to collaboratively train personalized models without revealing raw data. iPFL constructs a model-sharing market by solving a graph-based training optimization and incorporates an incentive mechanism based on game theory principles. Theoretical analysis shows that iPFL adheres to two key incentive properties: individual rationality and truthfulness. Empirical studies on eleven AI tasks (e.g., large language models' instruction-following tasks) demonstrate that iPFL consistently achieves the highest economic utility, and better or comparable model performance compared to baseline methods. We anticipate that our iPFL can serve as a valuable technique for boosting future AI models on decentralized private data while making everyone satisfied.
Paper Structure (10 sections, 4 theorems, 12 equations, 5 figures, 2 tables, 2 algorithms)

This paper contains 10 sections, 4 theorems, 12 equations, 5 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Results
  3. Performance Evaluation
  4. Incentive Properties
  5. Inclusive Market
  6. Discussion
  7. Methods
  8. Problem Formulation and Training Objective
  9. Training Procedure
  10. Theoretical Insights

Key Result

Theorem 1

If $\mathbf{A}^t$ is given by alg:thresh, then $\forall{i\in[m],t\in[T]}:U_i^t=G_i(\mathbf{a}^t_i)-\sum_{j\in[m]}{a^t_{ji}c_i}-p^t_i\ge0$.

Figures (5)

  • Figure 1: Inclusive PFL market and our iPFL. a. The clients have different purposes for entering a PFL system. A client can be: i) a trader who simultaneously buys model and sells their model; ii) a buyer who only buys a model and never shares its own model; iii) a seller who only sells its own model and never buys models; iv) an attacker who intends to ruin the system. b. In an inclusive market system, the model and money transaction should satisfy the needs of all the participants and block out attackers. c. In our iPFL, all the market behaviors are completed over a neutral server.
  • Figure 2: Comparison of average utility and accuracy in scatter under different settings. NIID represents the $\beta=0.1$, Cluster stands for 3 or 4 groups with $\beta=0.1$ among clusters, consider two cases with $\beta=10$ within each cluster; While in Skew, each client equally possesses data shards with 5 classes. Our iPFL achieves comparable or even better model performance and the highest utility across 9 settings.
  • Figure 3: The utility distribution of clients with different algorithms under three settings. Specifically, the circle denotes the mean utility of all clients, and the gray scatter represents the individual client utility values. Our iPFL guarantees positive utility for each client and achieves the highest average utility.
  • Figure 4: The change of average benign clients' performance (%) and malicious client utility after 4 different attack types of an attacker, under the Cluster setting on CIFAR-10. For each algorithm, we utilize the circle $\medbullet$ and star $\bigstar$ to separately represent the benign clients' states with their mean accuracy and utility.
  • Figure 5: The transaction graph of market simulation.

Theorems & Definitions (9)

  • Definition 1: utility
  • Definition 2: collaboration gain
  • Definition 3: sharing cost
  • Definition 4: overall payment
  • Definition 5: social welfare
  • Theorem 1: Individual Rationality
  • Lemma 1: Incentive Compatibility of $c_i$
  • Theorem 2: Truthfulness
  • Theorem 3: Robustness against abnormal data amount