Phd Position F/m Topology Design for Decentralized
il y a 3 semaines
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regiona economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
**Contexte et atouts du poste**:
The research activity will be supervised by
**Mission confiée**:
Topology Design for Decentralized Federated Learning
# Context
In the classic FL setting, a server coordinates the training phase. At each training round, the server sends the current model to the clients, which individually train on their local datasets and send model updates to the server, which in turn aggregates them (often through a simple averaging operation). In contrast to this client-server approach, decentralized FL algorithms (also called P2P FL algorithms) work by having each client communicate directly with a subset of the clients (its neighbours): this process alternates between model updates and weighted averaging of the neighbours' models (consensus-based optimization). Decentralized algorithms can take advantage of good pairwise connectivity, avoid the potential communication bottleneck at the server [marfoq20] as well as provide better privacy guarantees [cyffers22].
The communication graph (i.e., the graph induced by clients' pairwise communications) and the local clients' aggregation strategies play a fundamental role in determining FL algorithms' convergence speed. In particular, the communication topology has two contrasting effects on training time. First, a more connected topology leads to faster convergence in terms of number of communication rounds [nedic18]. Second, a more connected topology increases the duration of a communication round (e.g., because it may cause network congestion), motivating the use of degree-bounded topologies where every client sends and receives a small number of messages at each round [lian17]. Most of the existing literature has focused on one aspect or the other.
The classic literature on consensus-based optimization has quantified the effect of the communication topology on the number of rounds through worst-case convergence bounds in terms of the spectral gap of the consensus matrix (i.e., the matrix with the averaging weight), see [nedic18] and references there. Later papers have highlighted the convergence rate' insensitivity to the spectral gap for a large number of communication rounds and small learning rates [lian17,koloskova21,pu20].
Another line of work has shown that the effect of the topology is less important if local data distributions [neglia20] or average data distributions in each neighborhood [lebars23,dandi22] are close to the average data distribution over the whole population. In the extreme case of homogeneous local distributions, one may even prefer consensus matrices with poor spectral properties because they enable the use of larger learning rates [vogel22].
A separate line of works has studied how to design the communication topology in order to minimize the duration of one round, taking into account the variability of the computation times [neglia19] or the characteristics of Internet connections [marfoq20].
# Research objectives
The goal of this PhD is to propose algorithms to design the communication topology for decentralized federated learning with the goal of minimizing the total training duration, taking into account how connectivity affect both the number of rounds required and the duration of a single round.
Several settings will be considered: in particular, one may construct the topology in a pre-processing step (prior to learning), or dynamically while learning. Dynamic topology design can be a way to tackle online decentralized learning [asadi22,marfoq23], where the topology is adjusted and refined as clients collect more data.
Finally, he/she will also study to what extent the existing results can be extended to asymmetric communication links and other distributed optimization algorithms like push-sum ones [kempe03,benezit10].
# References
[asadi22] M. Asadi, A. Bellet, O.A. Maillard and M. Tommasi. Collaborative Algorith
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