Short: Gossip-Based Sampling in Social Overlays

Khelghatdoust, Mansour; Girdzijauskas, Šarūnas

doi:10.1007/978-3-319-09581-3_26

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Regarding the problem of achieving good information dissemination over a restricted communication topology, Khelghatdoust et al [16] propose a technique to build an efficient random overlay over a restricted network, by routing communication through multiple hops. The resulting overlay, being a random graph, allows efficient gossip learning, as shown in Section V. The drawback of this approach is the need to route the messages through intermediate nodes.…”

Section: Related Workmentioning

confidence: 99%

Gossip Learning: Off the Beaten Path

Giaretta

Girdzijauskas

2019

2019 IEEE International Conference on Big Data (Big Data)

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The growing computational demands of model training tasks and the increased privacy awareness of consumers call for the development of new techniques in the area of machine learning. Fully decentralized approaches have been proposed, but are still in early research stages. This study analyses gossip learning, one of these state-of-the-art decentralized machine learning protocols, which promises high scalability and privacy preservation, with the goal of assessing its applicability to realworld scenarios.Previous research on gossip learning presents strong and often unrealistic assumptions on the distribution of the data, the communication speeds of the devices and the connectivity among them. Our results show that lifting these requirements can, in certain scenarios, lead to slow convergence of the protocol or even unfair bias in the produced models. This paper identifies the conditions in which gossip learning can and cannot be applied, and introduces extensions that mitigate some of its limitations.

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Section: Related Workmentioning

confidence: 99%

Gossip Learning: Off the Beaten Path

Giaretta

Girdzijauskas

2019

2019 IEEE International Conference on Big Data (Big Data)

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“…The overall system can be thought of as a connected undirected graph with n vertices each representing a node. These nodes can be allowed to communicate randomly with any other node in the network, which shapes the underlying topology to a random graph [5,12,18,22]. Also, the communication among nodes can be restricted to enforce a specific underlying graph topology, for example the communication can be only allowed for friendship ties in social networks or among geographically co-located IoT devices [1,20,21].…”

Section: Introductionmentioning

confidence: 99%

Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters

Soliman

Girdzijauskas

Bouguelia

et al. 2020

Advances in Knowledge Discovery and Data Mining

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The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not only many privacy concerns, but also communication overhead. Therefore, in certain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or model aggregation. This paper presents a decentralized and adaptive k -means algorithm that clusters data from multiple sources organized in peer-topeer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k -means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k -means algorithms achieving accuracy gain that is up-to 36%.

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A Socially-Aware, Privacy-Preserving, and Scalable Federated Learning Protocol for Distributed Online Social Networks

Khelghatdoust

Mahdavi

2022

Advanced Information Networking and Applications

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Short: Gossip-Based Sampling in Social Overlays

Cited by 5 publications

References 6 publications

Gossip Learning: Off the Beaten Path

Gossip Learning: Off the Beaten Path

Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters

A Socially-Aware, Privacy-Preserving, and Scalable Federated Learning Protocol for Distributed Online Social Networks

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