A systematic literature review on federated machine learning: From a software engineering perspective
Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …
locally and formulate a global model based on the local model updates. To identify the state …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Fedml: A research library and benchmark for federated machine learning
Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …
existing FL libraries cannot adequately support diverse algorithmic development; …
Federated learning: Challenges, methods, and future directions
Federated learning involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while keeping data localized. Training in …
centers, such as mobile phones or hospitals, while keeping data localized. Training in …
Challenges, applications and design aspects of federated learning: A survey
Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …
the training of an algorithm across multiple decentralized edge devices or servers holding …
Federated learning architecture: Design, implementation, and challenges in distributed AI systems
Federated learning has emerged as a promising paradigm in the domain of distributed
artificial intelligence (AI) systems, enabling collaborative model training across …
artificial intelligence (AI) systems, enabling collaborative model training across …
A(DP)SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent With Differential Privacy
As deep learning models are usually massive and complex, distributed learning is essential
for increasing training efficiency. Moreover, in many real-world application scenarios like …
for increasing training efficiency. Moreover, in many real-world application scenarios like …
Byzantine-resilient decentralized stochastic gradient descent
Decentralized learning has gained great popularity to improve learning efficiency and
preserve data privacy. Each computing node makes equal contribution to collaboratively …
preserve data privacy. Each computing node makes equal contribution to collaboratively …
Resource-aware knowledge distillation for federated learning
The rise of deep learning and the Internet of Things (IoT) has driven a number of smart-world
applications, which are mostly deployed in distributed environments. Federated learning, a …
applications, which are mostly deployed in distributed environments. Federated learning, a …