8 papers
One-Shot Federated Learning with Classifier-Free Diffusion Models
Obaidullah Zaland, Shutong Jin, Florian T. Pokorny +1
Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clien…
Taming Cold Starts: Proactive Serverless Scheduling with Model Predictive Control
Chanh Nguyen, Monowar Bhuyan, Erik Elmroth
Serverless computing has transformed cloud application deployment by introducing a fine-grained, event-driven execution model that abstracts away infrastructure management. Its on-…
Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?
Obaidullah Zaland, Feras M. Awaysheh, Sawsan Al Zubi +2
Federated learning (FL) has emerged as a transformative paradigm for edge intelligence, enabling collaborative model training while preserving data privacy across distributed perso…
Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
Chanh Nguyen, Erik Elmroth, Monowar Bhuyan
Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility an…
Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges
Obaidullah Zaland, Chanh Nguyen, Florian T. Pokorny +1
Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad…
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping
Obaidullah Zaland, Erik Elmroth, Monowar Bhuyan
Federated Learning (FL) is a promising machine learning paradigm that enables participating devices to train privacy-preserved and collaborative models. FL has proven its benefits…