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cs.DC2026
Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
Amine Barrak
Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clients across aggregators, but eve…
cs.DC2025
Cost-Performance Analysis: A Comparative Study of CPU-Based Serverless and GPU-Based Training Architectures
Amine Barrak, Fabio Petrillo, Fehmi Jaafar
The field of distributed machine learning (ML) faces increasing demands for scalable and cost-effective training solutions, particularly in the context of large, complex models. Se…
cs.DC2025
Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
Amine Barrak, Emna Ksontini
As data-intensive applications grow, batch processing in limited-resource environments faces scalability and resource management challenges. Serverless computing offers a flexible…