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Found 3 papers, 1 papers with code
SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture
The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures.
Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning
In this paper, we introduce a novel architecture that combines serverless computing with P2P networks for distributed training and present a method for efficient parallel gradient computation under resource constraints.
Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance
However, the parameter server architecture may have limitations in terms of fault tolerance, including a single point of failure and complex recovery processes.
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