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Distributed artificial intelligence empowered by end-edge-cloud computing: A survey
As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …
also supports artificial intelligence evolving from a centralized manner to a distributed one …
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 …
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 …
Convergence of edge computing and deep learning: A comprehensive survey
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …
massive amounts of data, and ever-increasing computing power is driving the core of …
The right to be forgotten in federated learning: An efficient realization with rapid retraining
In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …
named machine unlearning, which enables data holders to proactively erase their data from …
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 …
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 …
Sharper convergence guarantees for asynchronous SGD for distributed and federated learning
We study the asynchronous stochastic gradient descent algorithm, for distributed training
over $ n $ workers that might be heterogeneous. In this algorithm, workers compute …
over $ n $ workers that might be heterogeneous. In this algorithm, workers compute …
SAFA: A semi-asynchronous protocol for fast federated learning with low overhead
Federated learning (FL) has attracted increasing attention as a promising approach to
driving a vast number of end devices with artificial intelligence. However, it is very …
driving a vast number of end devices with artificial intelligence. However, it is very …
Cocktailsgd: Fine-tuning foundation models over 500mbps networks
Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …
communication-intensive and so has heavily relied on centralized data centers with fast …