6 papers
Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
Obaidullah Zaland, Zulfiqar Ahmad Khan, Monowar Bhuyan
Modern big-data systems generate massive, heterogeneous, and geographically dispersed streams that are large-scale and privacy-sensitive, making centralization challenging. While f…
Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Obaidullah Zaland, Sajib Mistry, Monowar Bhuyan
Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables dece…
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…
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…
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…