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Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Promoting synergies for sustainability through peer-to-peer sharing: an analysis of drivers and barriers

Claudia Rebeca Binder Signer, Ralph Hansmann

Peer-to-peer (P2P) sharing may foster sustainability synergistically by saving resources, reducing costs and promoting satisfaction of consumer needs and social connectivity. A survey of Swiss and Swedish consumers (N = 1522) was conducted to analyze drive ...

TAYLOR & FRANCIS INC2023

Does Sharing Lead to Smarter Products? Managing Information Flows for Collective Servitization

Thomas Alois Weber

Peer-to-peer sharing induces persistent changes in product design. Besides bifurcating product durability, this adaptation increases the compatibility of collaborative use with rent extraction—from a producer’s viewpoint. For owners it decreases the commit ...

Springer2023

Shared metadata for data-centric materials science

Giovanni Pizzi, Ronald Earle Miller, Gian-Marco Rignanese, Carsten Baldauf, Matthias Scheffler, Tristan Bereau

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data prin ...

Nature Portfolio2023

Partial Information Sharing Over Social Learning Networks

Ali H. Sayed, Virginia Bordignon

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

A FAIR based approach to data sharing in Europe

Joan Decker

The European fusion research activities have, over recent decades, generated a vast and varied set of data. The volume and diversity of the data that need to be catalogued and annotated make the task of organising and making the data available within a bro ...

2022

Frugal Decentralized Learning

Anne-Marie Kermarrec

Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...

IEEE2022

Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls

Philip Egger, Friedhelm Christoph Hummel, Maximilian Jonas Wessel

Background: Noninvasive brain stimulation has been successfully applied to improve stroke impairments in different behavioral domains. Yet, clinical translation is limited by heterogenous comes within and across studies. It has been proposed to develop and ...

ELSEVIER SCIENCE INC2021

Nonlinear Pricing of Shareable Products

Thomas Alois Weber

We consider a durable-goods monopolist who is able to control the collaborative consumption of its goods on an aftermarket by a sharing tariff. Consumers are heterogeneous with respect to their respective need propensities in each period. We show that the ...

2020

Robust P2P Personalized Learning

Rachid Guerraoui, Yahya Benkaouz

Decentralized machine learning over peer-to-peer networks is very appealing for it enables to learn personalized models without sharing users data, nor relying on any central server. Peers can improve upon their locally trained model across a network graph ...

IEEE2020