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Distributed Machine Learning refers to the practice of training a model onmultiple computers or devices that can be called nodes. Additionally,serverless computing is a new paradigm for cloud computing that uses functionsas a computational unit. Serverless computing can be effective for distributedlearning systems by enabling automated resource scaling, less manualintervention, and cost reduction. By distributing the workload, distributedmachine learning can speed up the training process and allow more complexmodels to be trained. Several topologies of distributed machine learning havebeen established (centralized, parameter server, peer-to-peer). However, theparameter server architecture may have limitations in terms of fault tolerance,including a single point of failure and complex recovery processes. Moreover,training machine learning in a peer-to-peer (P2P) architecture can offerbenefits in terms of fault tolerance by eliminating the single point offailure. In a P2P architecture, each node or worker can act as both a serverand a client, which allows for more decentralized decision making andeliminates the need for a central coordinator. In this position paper, wepropose exploring the use of serverless computing in distributed machinelearning training and comparing the performance of P2P architecture with theparameter server architecture, focusing on cost reduction and fault tolerance.

Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance