Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives

TL;DR

This paper surveys the technical and legal challenges of creating open federated learning platforms, proposing new cooperation frameworks and a taxonomy for model license compatibility to enable more flexible and sustainable FL systems.

Contribution

It introduces a comprehensive review of open FL platform feasibility, proposes query-based and contract-based cooperation frameworks, and develops a taxonomy for model license analysis.

Findings

Identifies limitations of traditional FL such as server-client coupling.

Proposes two reciprocal cooperation frameworks for open FL.

Develops a taxonomy for analyzing model license compatibility.

Abstract

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to participate. To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users. We propose two reciprocal cooperation frameworks to achieve this: query-based FL and contract-based FL. In this survey, we conduct a comprehensive review of the feasibility of constructing open FL platforms from both technical and legal perspectives. We begin by reviewing the definition of FL and summarizing its inherent limitations, including server-client coupling, low model reusability, and non-public. In particular, we introduce a novel taxonomy to streamline the analysis of model license compatibility in FL studies that involve batch model reusing methods, including combination, amalgamation, distillation, and generation. This taxonomy provides a feasible solution for identifying the corresponding licenses clauses and facilitates the analysis of potential legal implications and restrictions when reusing models. Through this survey, we uncover the current dilemmas faced by FL and advocate for the development of sustainable open FL platforms. We aim to provide guidance for establishing such platforms in the future while identifying potential limitations that need to be addressed.