Confluence of Random Walks, Interacting Particle Systems, and Distributed Machine Learning: Federated Learning through Crawling over Networks

Authors

  • Seyyedali Hosseinalipour University at Buffalo–SUNY

DOI:

https://doi.org/10.1609/aaaiss.v3i1.31216

Keywords:

ML: Distributed Machine Learning & Federated Learning, Collaborative Learning, Learning On The Edge

Abstract

In this work, we aim to unveil a new class of intermediate FL architectures between centralized and decentralized schemes called “FedCrawl.” FedCrawl takes advantage of benefits of D2D communications similar to decentralized schemes; however, it uses them in a nuanced way. FedCrawl is inspired by web crawlers, which effectively explore the websites to find updated/new content posted on the internet. The cornerstone of FedCrawl is its innovative conceptualization of neural networks (NNs) or other used ML models as autonomous entities, called random walkers, with the capability to move or jump across nodes in the network through peer-to-peer (P2P) or device-to-device (D2D) connections. We introduce five research aspects to study the nuanced intricacies governing random walker behavior in these environments. The first research aspect addresses the interplay between network topology and data distribution, emphasizing the importance of considering both factors for designing efficient random walks in FedCrawl. The second research aspect explores the applicability of node importance metrics in optimizing random walker paths for FedCrawl. We propose a dynamic perception-aware design, discussed in the third research aspect, where transition matrices adapt to the evolving state of random walkers, balancing exploration and exploitation. The fourth research aspect introduces innovative features like skipping, memory look-back, and caching/trailing to enhance random walker performance. Lastly, the fifth research aspect delves into the dynamics of multiple random walkers in networked environments, introducing the concept of multi-pole random walkers. Complementing these five research aspects, we present five conjectures, each introducing novel perspectives and methodologies in the domain of decentralized learning. These conjectures encompass areas such as temperature-based characterization of random walkers and network nodes, dynamic transition matrices, non-Markovian processes, and an evolutionary framework for random walker patterns.

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Published

2024-05-20