Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
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 learning with differential privacy: Algorithms and performance analysis
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
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 …
Local SGD converges fast and communicates little
SU Stich - ar** machine learning algorithms. To address the …
Fully decentralized multi-agent reinforcement learning with networked agents
We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …
where the agents are connected via a time-varying and possibly sparse communication …
Federated optimization: Distributed machine learning for on-device intelligence
We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …
learning, where the data defining the optimization are unevenly distributed over an …
Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are
built in a centralized fashion. One bottleneck of centralized algorithms lies on high …
built in a centralized fashion. One bottleneck of centralized algorithms lies on high …