A survey on federated learning for resource-constrained IoT devices
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …
model by learning from multiple decentralized edge clients. FL enables on-device training …
Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …
applications. Accelerating their training is a major challenge and techniques range from …
Federated multi-task learning under a mixture of distributions
The increasing size of data generated by smartphones and IoT devices motivated the
development of Federated Learning (FL), a framework for on-device collaborative training of …
development of Federated Learning (FL), a framework for on-device collaborative training of …
Federated learning with buffered asynchronous aggregation
Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …
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 …
[HTML][HTML] Privacy preservation in federated learning: An insightful survey from the GDPR perspective
In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …
services, ensuring data privacy and security have become a critical obligation. ML-based …
Asynchronous federated optimization
Federated learning enables training on a massive number of edge devices. To improve
flexibility and scalability, we propose a new asynchronous federated optimization algorithm …
flexibility and scalability, we propose a new asynchronous federated optimization algorithm …
Decentralized federated averaging
Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …
Adaptive federated learning in resource constrained edge computing systems
Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …
crowd-sourcing generate large amounts of data at the network edge. Machine learning …
Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning
In distributed training of deep neural networks, parallel minibatch SGD is widely used to
speed up the training process by using multiple workers. It uses multiple workers to sample …
speed up the training process by using multiple workers. It uses multiple workers to sample …