260 citations · 493 across the 27 of their papers we have counts for
15 papers · 1 filter
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…
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…
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
Qilei Li, Ahmed M. Abdelmoniem
Federated Learning (FL) is a distributed machine learning diagram that enables multiple clients to collaboratively train a global model without sharing their private local data. Ho…
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
Shaoyuan Chen, Linlin You, Rui Liu +2
The training of large models, involving fine-tuning, faces the scarcity of high-quality data. Compared to the solutions based on centralized data centers, updating large models in…
Flashback: Understanding and Mitigating Forgetting in Federated Learning
Mohammed Aljahdali, Ahmed M. Abdelmoniem, Marco Canini +1
In Federated Learning (FL), forgetting, or the loss of knowledge across rounds, hampers algorithm convergence, particularly in the presence of severe data heterogeneity among clien…
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms
Sofia Zahri, Hajar Bennouri, Ahmed M. Abdelmoniem
In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogen…
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction
Karan Gadgil, Sukhpal Singh Gill, Ahmed M. Abdelmoniem
Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue and this could be achieved only by understanding the customers more.…
Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data
Ahmed M. Abdelmoniem
With mobile, IoT and sensor devices becoming pervasive in our life and recent advances in Edge Computational Intelligence (e.g., Edge AI/ML), it became evident that the traditional…
Towards Energy-Aware Federated Learning on Battery-Powered Clients
Amna Arouj, Ahmed M. Abdelmoniem
Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with pr…
Resource-Efficient Federated Learning
Ahmed M. Abdelmoniem, Atal Narayan Sahu, Marco Canini +1
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges re…
Rethinking gradient sparsification as total error minimization
Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem +3
Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback fr…
On the Impact of Device and Behavioral Heterogeneity in Federated Learning
Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou +2
Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities. FL has seen successful deployme…
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems
Ahmed M. Abdelmoniem, Ahmed Elzanaty, Mohamed-Slim Alouini +1
The recent many-fold increase in the size of deep neural networks makes efficient distributed training challenging. Many proposals exploit the compressibility of the gradients and…