2 citations · 7 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture
Amine Barrak, Mayssa Jaziri, Ranim Trabelsi +2
The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures. Yet, the integrati…
Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning
Amine Barrak, Ranim Trabelsi, Fehmi Jaafar +1
The increasing demand for computational power in big data and machine learning has driven the development of distributed training methodologies. Among these, peer-to-peer (P2P) net…
Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance
Amine Barrak, Fabio Petrillo, Fehmi Jaafar
Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new parad…