English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The advent of serverless computing has ushered in notable advancements indistributed machine learning, particularly within parameter server-basedarchitectures. Yet, the integration of serverless features within peer-to-peer(P2P) distributed networks remains largely uncharted. In this paper, weintroduce SPIRT, a fault-tolerant, reliable, and secure serverless P2P MLtraining architecture. designed to bridge this existing gap.  Capitalizing on the inherent robustness and reliability innate to P2Psystems, SPIRT employs RedisAI for in-database operations, leading to an 82\%reduction in the time required for model updates and gradient averaging acrossa variety of models and batch sizes. This architecture showcases resilienceagainst peer failures and adeptly manages the integration of new peers, therebyhighlighting its fault-tolerant characteristics and scalability. Furthermore,SPIRT ensures secure communication between peers, enhancing the reliability ofdistributed machine learning tasks. Even in the face of Byzantine attacks, thesystem's robust aggregation algorithms maintain high levels of accuracy. Thesefindings illuminate the promising potential of serverless architectures in P2Pdistributed machine learning, offering a significant stride towards thedevelopment of more efficient, scalable, and resilient applications.

SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture