5 papers
Decentralized Ranking Aggregation via Gossip: Convergence and Robustness
Kerrian Le Caillec, Anna Van Elst, Igor Colin +1
The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory,…
Fast and Efficient Gossip Algorithms for Robust and Non-smooth Decentralized Learning
Anna van Elst, Igor Colin, Stephan Clémençon
Decentralized learning on resource-constrained edge devices demands algorithms that are communication-efficient, robust to data corruption, and lightweight in memory. State-of-the-…
On Gossip Algorithms for Machine Learning with Pairwise Objectives
Igor Colin, Aurélien Bellet, Stephan Clémençon +1
In the IoT era, information is more and more frequently picked up by connected smart sensors with increasing, though limited, storage, communication and computation abilities. Whet…
Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
Anna Van Elst, Igor Colin, Stephan Clémençon
This paper addresses the problem of robust estimation in gossip algorithms over arbitrary communication graphs. Gossip algorithms are fully decentralized, relying only on local nei…
Asynchronous Gossip Algorithms for Rank-Based Statistical Methods
Anna Van Elst, Igor Colin, Stephan Clémençon
As decentralized AI and edge intelligence become increasingly prevalent, ensuring robustness and trustworthiness in such distributed settings has become a critical issue-especially…