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

服务无服务器计算为分布式机器学习带来了显著的进展，特别是在基于参数服务器的体系结构中。然而，在点对点（P2P）分布式网络中整合服务器特性仍然是一个未被开发的领域。本文介绍了 SPIRT，一个容错、可靠、安全的无服务器 P2P 机器学习训练架构，旨在填补现有差距。SPIRT 利用 P2P 系统天然的韧性和可靠性，在数据库操作中使用 RedisAI，从而大幅度降低了模型更新和梯度平均所需的时间，在多种模型和批次大小上都取得了 82% 的降低。该架构展示了对节点故障的容错能力，并熟练地管理了新节点的整合，从而突出了其容错特性和可扩展性。此外，SPIRT 确保节点之间的安全通信，增强了分布式机器学习任务的可靠性。即使面对拜占庭攻击，系统的强大聚合算法仍然保持高水平的准确性。这些发现揭示了无服务器架构在 P2P 分布式机器学习中的巨大潜力，为开发更高效、可扩展和具有韧性的应用程序迈出了重要的步伐。