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Found 7 papers, 4 papers with code
Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes
We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between participating nodes.
Low-Cost Privacy-Aware Decentralized Learning
This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that relies on adding correlated noise to each model update during the model training process.
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches.
Get More for Less in Decentralized Learning Systems
Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality.
Decentralized Learning Made Easy with DecentralizePy
Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance.
TEE-based decentralized recommender systems: The raw data sharing redemption
We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of REX.
Boosting Federated Learning in Resource-Constrained Networks
GeL enables constrained edge devices to perform additional learning through guessed updates on top of gradient-based steps.
