94 citations · 355 across the 30 of their papers we have counts for
34 papers
Performance Model for Similarity Caching
Younes Ben Mazziane, Sara Alouf, Giovanni Neglia +1
Similarity caching allows requests for an item to be served by a similar item. Applications include recommendation systems, multimedia retrieval, and machine learning. Recently, ma…
FedDec: Peer-to-peer Aided Federated Learning
Marina Costantini, Giovanni Neglia, Thrasyvoulos Spyropoulos
Federated learning (FL) has enabled training machine learning models exploiting the data of multiple agents without compromising privacy. However, FL is known to be vulnerable to d…
Federated Learning under Heterogeneous and Correlated Client Availability
Angelo Rodio, Francescomaria Faticanti, Othmane Marfoq +2
The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collabora…
Federated Learning for Data Streams
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni +1
Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data…
Local Model Reconstruction Attacks in Federated Learning and their Uses
Ilias Driouich, Chuan Xu, Giovanni Neglia +2
In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops the messages exchanged between a…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Computing the Hit Rate of Similarity Caching
Younes Ben Mazziane, Sara Alouf, Giovanni Neglia +1
Similarity caching allows requests for an item \(i\) to be served by a similar item \(i'\). Applications include recommendation systems, multimedia retrieval, and machine learning.…
Enabling Long-term Fairness in Dynamic Resource Allocation
T. Si-Salem, G. Iosifidis, G. Neglia
We study the fairness of dynamic resource allocation problem under the -fairness criterion. We recognize two different fairness objectives that naturally arise in this problem:…
A Formal Analysis of the Count-Min Sketch with Conservative Updates
Younes Ben Mazziane, Sara Alouf, Giovanni Neglia
Count-Min Sketch with Conservative Updates (CMS-CU) is a popular algorithm to approximately count items' appearances in a data stream. Despite CMS-CU's widespread adoption, the the…
Personalized Federated Learning through Local Memorization
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni +1
Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model…
Efficient passive membership inference attack in federated learning
Oualid Zari, Chuan Xu, Giovanni Neglia
In cross-device federated learning (FL) setting, clients such as mobiles cooperate with the server to train a global machine learning model, while maintaining their data locally. H…
Federated Multi-Task Learning under a Mixture of Distributions
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet +2
The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machi…
Ascent Similarity Caching with Approximate Indexes
T. Si-Salem, G. Neglia, D. Carra
Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented real…
A New Upper Bound on Cache Hit Probability for Non-anticipative Caching Policies
Nitish K. Panigrahy, Philippe Nain, Giovanni Neglia +1
Caching systems have long been crucial for improving the performance of a wide variety of network and web based online applications. In such systems, end-to-end application perform…
Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guarantees
T. Si Salem, G. Castellano, G. Neglia +2
An increasing number of applications rely on complex inference tasks that are based on machine learning (ML). Currently, there are two options to run such tasks: either they are se…
Content Placement in Networks of Similarity Caches
Michele Garetto, Emilio Leonardi, Giovanni Neglia
Similarity caching systems have recently attracted the attention of the scientific community, as they can be profitably used in many application contexts, like multimedia retrieval…
No-Regret Caching via Online Mirror Descent
T. Si Salem, G. Neglia, S. Ioannidis
We study an online caching problem in which requests can be served by a local cache to avoid retrieval costs from a remote server. The cache can update its state after a batch of r…
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Othmane Marfoq, Chuan Xu, Giovanni Neglia +1
Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model.…
Dynamic backup workers for parallel machine learning
Chuan Xu, Giovanni Neglia, Nicola Sebastianelli
The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of workers, which iteratively…
Decentralized gradient methods: does topology matter?
Giovanni Neglia, Chuan Xu, Don Towsley +1
Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine…
Similarity Caching: Theory and Algorithms
Michele Garetto, Emilio Leonardi, Giovanni Neglia
This paper focuses on similarity caching systems, in which a user request for an {object~} that is not in the cache can be (partially) satisfied by a similar stored {object~…
How often should I access my online social networks?
Eduardo Hargreaves, Daniel Sadoc Menasché, Giovanni Neglia
Users of online social networks are faced with a conundrum of trying to be always informed without having enough time or attention budget to do so. The retention of users on online…
Fairness in Online Social Network Timelines: Measurements, Models and Mechanism Design
Eduardo Hargreaves, Claudio Agosti, Daniel Menasché +3
Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior…
Feedforward Neural Networks for Caching: Enough or Too Much?
Vladyslav Fedchenko, Giovanni Neglia, Bruno Ribeiro
We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a…