Toward an Automated Auction Framework for Wireless Federated Learning Services Market (1912.06370v2)

Abstract: In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

• The paper presents an innovative auction framework integrating DRL and GNN to efficiently allocate wireless FL resources while maximizing social welfare.
• It introduces the Reverse Multi-dimensional Auction mechanism that groups data owners by EMD values to optimize marginal social welfare and ensure strategy-proofness.
• The approach achieves conflict-aware resource allocation and robust incentive designs, validated by empirical analysis and theoretical proofs.

An Automated Auction Framework for Wireless Federated Learning Services

This paper introduces an innovative framework for the commercialization of federated learning (FL) services over wireless networks, focusing on the development of auction mechanisms to efficiently allocate resources and incentivize participation. The purpose of this research is to maximize social welfare within the FL ecosystem while maintaining truthfulness and individual rationality among data owners who contribute their data, computation, and communication resources to the federated learning process.

Federated learning addresses data privacy concerns by allowing model training to occur locally on devices without centralizing the data. A critical aspect of federated learning is the need for sufficient data owners to partake in the learning process constructively. The paper proposes an auction-based market model to stimulate data owner participation, presenting two mechanisms aimed at optimizing social welfare within this market: the Reverse Multi-dimensional Auction (RMA) mechanism and an automated auction mechanism based on Deep Reinforcement Learning (DRLA) integrated with Graph Neural Networks (GNN).

The RMA mechanism is designed as an approximate strategy-proof approach that divides data owners into groups based on their data's EMD value, a metric that quantifies the non-IID nature of data. This mechanism sorts and selects workers by evaluating the marginal social welfare density, ensuring that social welfare is maximized without compromising the economic properties essential for market stability.

The DRLA mechanism enhances social welfare by leveraging the capabilities of deep learning and graph-based embeddings to represent channel conflicts more effectively and choose workers through a sequential process that intelligently evaluates a broader set of feature inputs. It incorporates state-of-the-art machine learning techniques to achieve superior resource allocation decisions.

Several key findings and contributions are highlighted within the paper:

• Verification of the proposed data utility function through empirical analysis, demonstrating alignment with real-world data regarding the impacts of data volume and its distribution on federated learning outcomes.
• Introduction of a conflict-aware automatic auction design using DRL and GNN, significantly improving social welfare compared to traditional auction approaches.
• Theoretical proofs validating the strategy-proof nature of both proposed mechanisms, reinforcing their feasibility and reliability in practical deployments.

The implications of this work extend to the broader AI field, as efficient federated learning mechanisms tailored to the wireless environment can significantly contribute to the realization of privacy-preserving models across decentralized networks. Moreover, integrating AI-driven auction strategies has potential applications beyond FL, in areas needing robust resource allocation solutions, like edge computing and autonomous networks.

Looking forward, the paper suggests continued exploration of automated auction mechanisms in federated learning, considering diverse network conditions and increasingly sophisticated models. Future developments may involve exploring hybrid strategies combining aspects of DRL and optimization theory to handle more complex multi-agent interactions and competitive market environments. The proposed research lays a foundational pathway for enhancing federated learning systems, and it opens avenues for further AI-driven innovations in the context of distributed machine learning.