The increasing demand for computational power in big data and machine
learning has driven the development of distributed training methodologies.
Among these, peer-to-peer (P2P) networks provide advantages such as enhanced
scalability and fault tolerance. However, they also encounter challenges
related to resource consumption, costs, and communication overhead as the
number of participating peers grows. 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.
Our findings show a significant enhancement in gradient computation time,
with up to a 97.34\% improvement compared to conventional P2P distributed
training methods. As for costs, our examination confirmed that the serverless
architecture could incur higher expenses, reaching up to 5.4 times more than
instance-based architectures. It is essential to consider that these higher
costs are associated with marked improvements in computation time, particularly
under resource-constrained scenarios. Despite the cost-time trade-off, the
serverless approach still holds promise due to its pay-as-you-go model.
Utilizing dynamic resource allocation, it enables faster training times and
optimized resource utilization, making it a promising candidate for a wide
range of machine learning applications