Communication Dans Un Congrès Année : 2025

Decolonizing Federated Learning: Designing Fair and Responsible Resource Allocation

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This position paper explores the challenges, existing solutions, and open issues related to resource allocation in federated learning environments. The focus is on how to allocate resources effectively while adhering to service level objectives (SLOs) and fairness requirements, which include factors such as server location, data provenance, energy consumption, sovereignty, carbon footprint, and economic cost. The goal is to optimise resource distribution across different stages of the federated learning process within a given architecture, ensuring that these fairness criteria are integrated into the allocation strategy. This approach aligns with decolonial methodologies that seek to offer more sustainable and equitable alternatives to the resource-intensive artificial intelligence processes prevalent today.

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hal-04743266 , version 1 (18-10-2024)

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Genoveva Vargas-Solar, Javier-Alfonso Espinosa-Oviedo, Nadia Bennani, Andrea Mauri, José-Luis Zechinelli-Martini, et al.. Decolonizing Federated Learning: Designing Fair and Responsible Resource Allocation. ACS/IEEE 21st International Conference on Computer Systems and Applications (AICCSA 2024), Oct 2024, Sousse, Tunisia. pp.1-7, ⟨10.1109/AICCSA63423.2024.10912594⟩. ⟨hal-04743266⟩
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