Search Results for author:
Found 16 papers, 6 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.
Fairness Auditing with Multi-Agent Collaboration
We theoretically study their interplay when agents operate independently or collaborate.
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.
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.
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents.
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.
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning
The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized setting without a central server.
Cluster-and-Conquer: When Randomness Meets Graph Locality
K-Nearest-Neighbors (KNN) graphs are central to many emblematic data mining and machine-learning applications.
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction
Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local.
Derived Codebooks for High-Accuracy Nearest Neighbor Search
We propose a novel approach that allows 16-bit quantizers to offer the same response time as 8-bit quantizers, while still providing a boost of accuracy.
Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD
Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems.
Accelerated Nearest Neighbor Search with Quick ADC
This allows Quick ADC to exceed the performance of state-of-the-art systems, e. g., it achieves a Recall@100 of 0. 94 in 3. 4 ms on 1 billion SIFT descriptors (128-bit codes).
