Anne-Marie Kermarrec

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This paper defines the problem of Scalable Secure computing in a Social network: we call it the S-3 problem. In short, nodes, directly reflecting on associated users, need to compute a symmetric function f : V-n -> U of their inputs in a set of constant si ...

Federated Learning (FL) has gained considerable attention recently, as it allows clients to cooperatively train a global machine learning model without sharing raw data. However, its performance can be compromised due to the high heterogeneity in clients' ...

This paper presents GosSkip, a self organizing and fully distributed overlay that provides a scalable support to data storage and retrieval in dynamic environments. The structure of GosSkip, while initially possibly chaotic, eventually matches a perfect se ...

The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-independent and identically distributed (non-IID) data partitions, even more so in a fully decentralized setting without a central server. In ...

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. Standard FL infrastructures are however designed to have no energy or ...

Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

Recommenders, as widely implemented nowadays by major e-commerce players like Netflix or Amazon, use collaborative filtering to suggest the most relevant items to their users. Clearly, the effectiveness of recommenders depends on the data they can exploit, ...

The ability to identify people that share one's own interests is one of the most interesting promises of the Web 2.0 driving user-centric applications such as recommendation systems or collaborative marketplaces. To be truly useful, however, information ab ...

Gossip-based protocols have proven very efficient for disseminating high-bandwidth content such as video streams in a peer-to-peer fashion. However, for the protocols to work, nodes are required to collaborate by devoting a fraction of their upload bandwid ...

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 ...

This paper addresses the problem of efficient scheduling of large clusters under high load and heterogeneous workloads. A heterogeneous workload typically consists of many short jobs and a small number of large jobs that consume the bulk of the cluster's r ...

We consider the polling problem in a social network where participants care about their reputation: they do not want their vote to be disclosed nor their misbehaving, if any, to be publicly exposed. Assuming this reputation concern, we show that a simple s ...

We present the design, correctness, and analysis of SONDe, a simple fully decentralized object deployment algorithm for highly requested systems. Given an object (service or data), SONDe provides a node with a constant upper bound (h) on the number of logi ...

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distanc ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no un ...

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard's index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

So far, the distributed computing community has either assumed that all the processes of a distributed system have distinct identifiers or, more rarely, that the processes are anonymous and have no identifiers. These are two extremes of the same general mo ...

Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance. It consists of many nodes that coordinate without a central server and exchange millions of parameters in the inherently ite ...

Anne-Marie Kermarrec

Chat with Graph Search

Computing in social networks

Rethinking Personalized Client Collaboration in Federated Learning

GosSkip, an Efficient, Fault-Tolerant and Self Organizing Overlay Using Gossip-based Construction and Skip-Lists principles

D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Practical Federated Learning without a Server

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

Trust-aware peer sampling: Performance and privacy tradeoffs

Tracking freeriders in gossip-based content dissemination systems

Frugal Decentralized Learning

Hawk: Hybrid Datacenter Scheduling

Decentralized Polling with Respectable Participants

SONDe, a Self-Organizing Object Deployment Algorithm in Large-Scale Dynamic Systems

Quicker ADC : Unlocking the Hidden Potential of Product Quantization With SIMD

Fair Decentralized Learning

ProteusTM: Abstraction Meets Performance in Transactional Memory

GoldFinger: Fast & Approximate Jaccard for Efficient KNN Graph Constructions

Smaller, Faster & Lighter KNN Graph Constructions

Byzantine Agreement with Homonyms

Decentralized learning made easy with DecentralizePy