Towards Open Federated Learning Platforms: Survey and Vision from
Technical and Legal Perspectives
- URL: http://arxiv.org/abs/2307.02140v3
- Date: Thu, 29 Feb 2024 14:42:23 GMT
- Title: Towards Open Federated Learning Platforms: Survey and Vision from
Technical and Legal Perspectives
- Authors: Moming Duan, Qinbin Li, Linshan Jiang, Bingsheng He
- Abstract summary: Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm.
We advocate rethinking the design of current FL frameworks and extending it to a more general concept: Open Federated Learning Platforms.
- Score: 34.0620974123791
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework [1.4206132527980742]
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy.
We present the recent advances in developing APPFL, a framework and benchmarking suite for federated learning.
We demonstrate the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization.
arXiv Detail & Related papers (2024-09-17T22:20:26Z) - Deep Equilibrium Models Meet Federated Learning [71.57324258813675]
This study explores the problem of Federated Learning (FL) by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks.
We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL.
To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning.
arXiv Detail & Related papers (2023-05-29T22:51:40Z) - Bayesian Federated Learning: A Survey [54.40136267717288]
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner.
The robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions.
BFL has emerged as a promising approach to address these issues.
arXiv Detail & Related papers (2023-04-26T03:41:17Z) - Vertical Federated Learning: A Structured Literature Review [0.0]
Federated learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy.
In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL.
arXiv Detail & Related papers (2022-12-01T16:16:41Z) - ModularFed: Leveraging Modularity in Federated Learning Frameworks [8.139264167572213]
We propose a research-focused framework that addresses the complexity of Federated Learning (FL) implementations.
Within this architecture, protocols are blueprints that strictly define the framework's components' design.
Our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators.
arXiv Detail & Related papers (2022-10-31T10:21:19Z) - UniFed: All-In-One Federated Learning Platform to Unify Open-Source
Frameworks [53.20176108643942]
We present UniFed, the first unified platform for standardizing open-source Federated Learning (FL) frameworks.
UniFed streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks.
We evaluate and compare 11 popular FL frameworks from the perspectives of functionality, privacy protection, and performance.
arXiv Detail & Related papers (2022-07-21T05:03:04Z) - FederatedScope: A Comprehensive and Flexible Federated Learning Platform
via Message Passing [63.87056362712879]
We propose a novel and comprehensive federated learning platform, named FederatedScope, which is based on a message-oriented framework.
Compared to the procedural framework, the proposed message-oriented framework is more flexible to express heterogeneous message exchange.
We conduct a series of experiments on the provided easy-to-use and comprehensive FL benchmarks to validate the correctness and efficiency of FederatedScope.
arXiv Detail & Related papers (2022-04-11T11:24:21Z) - Towards Verifiable Federated Learning [15.758657927386263]
Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models.
Due to the nature of open participation by self-interested entities, FL needs to guard against potential misbehaviours by legitimate FL participants.
Verifiable federated learning has become an emerging topic of research that has attracted significant interest from the academia and the industry alike.
arXiv Detail & Related papers (2022-02-15T09:52:25Z) - Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn.
We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
arXiv Detail & Related papers (2020-12-01T11:46:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.