7 papers
DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices
Songyuan Li, Jia Hu, Ahmed M. Abdelmoniem +3
Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these m…
Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions
Wentai Wu, Ligang He, Saiqin Long +4
Increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although Federated Learning-based…
Domain-Agnostic Causal-Aware Audio Transformer for Infant Cry Classification
Geofrey Owino, Bernard Shibwabo Kasamani, Ahmed M. Abdelmoniem +1
Accurate and interpretable classification of infant cry paralinguistics is essential for early detection of neonatal distress and clinical decision support. However, many existing…
Benchmarking Mutual Information-based Loss Functions in Federated Learning
Sarang S, Harsh D. Chothani, Qilei Li +2
Federated Learning (FL) has attracted considerable interest due to growing privacy concerns and regulations like the General Data Protection Regulation (GDPR), which stresses the i…
Query-based Knowledge Transfer for Heterogeneous Learning Environments
Norah Alballa, Wenxuan Zhang, Ziquan Liu +3
Decentralized collaborative learning under data heterogeneity and privacy constraints has rapidly advanced. However, existing solutions like federated learning, ensembles, and tran…
FLStore: Efficient Federated Learning Storage for non-training workloads
Ahmad Faraz Khan, Samuel Fountain, Ahmed M. Abdelmoniem +2
Federated Learning (FL) is an approach for privacy-preserving Machine Learning (ML), enabling model training across multiple clients without centralized data collection. With an ag…