Decolonizing Federated Learning: Designing Fair and Responsible Resource Allocation

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.