Federated Machine Learning: Concept and Applications
Abstract
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.
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A secure aggregation scheme for safe FL systems is proposed to provide the strongest possible security with respect to server-mediated, unauthenticated network conditions.
“Moreover, the study in [191] proposes a secure aggregation scheme for safe FL systems, in order to provide the strongest possible security with respect server-mediated, unauthenticated network conditions.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [191]
In some specific learning scenarios, such as federated learning, the training dataset may not be accessible, and thus retraining cannot be conducted at all.
“Moreover, in some specific learning scenarios, such as federated learning [31, 32], the training dataset may not be accessible, and thus retraining cannot be conducted at all.”
From Machine Unlearning: A Survey (2024) · cites this paper as [32]
TFedRec trains a model by transferring knowledge from source to target domain parties without revealing user privacy.
“TFedRec trains a model by transferring knowledge from source to target domain parties without revealing user privacy [135-137].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [137]
Model training can still be scheduled on fog nodes by leveraging federated learning.
“Nevertheless, model training can still be scheduled on fog nodes by leveraging federated learning [60].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [60]
Different data owners collaboratively train a model, whose performance is expected to match the performance of the model directly trained on the union of all data [60].
“Different data owners collaboratively train a model, whose performance is expected to match the performance of the model directly trained on the union of all data [60].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [60]
Accumulation of system or model parameters creates a global model using a server.
“Accumulation of system or model parameters creates a global model using a server [72].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [72]
In highly heterogeneous FL environments, FedProx demonstrates significantly more steady and accurate convergence behavior relative to FedAvg.
“In highly heterogeneous FL environments, FedProx demonstrates significantly more steady and accurate convergence behavior relative to FedAvg.”
From Vulnerabilities in federated learning (2021) · cites this paper as [40]
FedProx was introduced as a generalization and re-parametrization of FedAvg.
“FedProx [40] was introduced as a generalization and re-parametrization of FedAvg.”
From Vulnerabilities in federated learning (2021) · cites this paper as [40]
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- Authors
- Yang, Q., Liu, Y., Chen, T., & Tong, Y.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Webank, Shenzhen, China
- Beihang University (CN)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2019-01-28
- Volume
- 10
- Issue
- 2
- Pages
- 1-19
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2912213068
- Cited by
- 6,322
- References
- 79
- Citations by year
- 2026: 943 · 2025: 1229 · 2024: 1009 · 2023: 946 · 2022: 816 · 2021: 875 · 2020: 429 · 2019: 74 · 2018: 1
- FWCI
- 250.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, secure federated learning, horizontal federated learning, vertical federated learning, federated transfer learning, data privacy, data security, data silos, inter-organizational data sharing, knowledge sharing
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