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Found 6 papers, 3 papers with code
Fairness Auditing with Multi-Agent Collaboration
Existing work in fairness auditing assumes that each audit is performed independently.
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation
We introduce QuickDrop, an efficient and original FU method that utilizes dataset distillation (DD) to accelerate unlearning and drastically reduces computational overhead compared to existing 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.
