Optimization Algorithms for Split Learning

Información del proyecto

OPALS

Identificador del acuerdo de subvención: 101210495

DOI

10.3030/101210495

Fecha de la firma de la CE 19 Marzo 2025

Fecha de inicio 1 Abril 2025

Fecha de finalización 31 Marzo 2027

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 194 074,56

Coordinado por

TELEFONICA INNOVACION DIGITAL SL
Spain

Objetivo

With the rapid evolution of Artificial Intelligence, distributed machine learning methods such as Federated Learning (FL) are becoming ubiquitous in present-day technology. In FL, devices train neural network models while data stays local. A central entity then aggregates the model updates into a global model. Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to participate in this learning framework. In a nutshell, SL splits the model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper (edge server, cloud, or other devices). Essentially, SL is a paradigm shift offering a more flexible version of FL that alleviates the load at the devices by better utilizing other available resources in the network. However, this method comes with optimization challenges since networking decisions need to be made in order to orchestrate the SL operations and overcome any communication overhead. Despite the increasing attention towards SL, current algorithms focus on minimizing the training time and improving on energy efficiency, without however bearing any performance guarantees. In order to make SL efficient, OPALS will fill this crucial gap by providing algorithms with provable guarantees. In particular, OPALS focuses on 3 main research axes. First, it studies the well-established problem of minimizing the training time, in search of the first algorithm with guarantees. Second, it seeks ways of leveraging SL to reduce the carbon footprint of distributed learning. Third, it investigates how SL could be employed in a decentralized setting in view of the increasing importance of swarm intelligence. OPALS will employ mathematical modelling and cutting-edge optimization methods to achieve these goals. As a result, OPALS will pave the way to better resource utilization, and thus, efficient SL exploitable for technological innovation.

Palabras clave

• split learning
• federated learning
• decentralized learning
• approximation algorithms
• carbon-aware algorithms

Programa(s)

• HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) PROGRAMA PRINCIPAL
  

Tema(s)

• HORIZON-MSCA-2024-PF-01-01 - MSCA Postdoctoral Fellowships 2024
  

Régimen de financiación

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

Convocatoria de propuestas

HORIZON-MSCA-2024-PF-01

Coordinador

TELEFONICA INNOVACION DIGITAL SL

• Aportación neta de la UEn: € 194 074,56
• Dirección: RONDA DE LA COMUNICACION S/N EDIFICO CENTRAL ; 28050 MADRID ; España
• Región: Comunidad de Madrid › Comunidad de Madrid › Madrid
• Tipo de actividad: Private for-profit entities (excluding Higher or Secondary Education Establishments)
• Coste total: Sin datos