260 citations · 493 across the 27 of their papers we have counts for
28 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…
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…
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…
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…
A Novel Approach to Translate Structural Aggregation Queries to MapReduce Code
Ahmed M. Abdelmoniem, Sameh Abdulah, Walid Atwa
Data management applications are growing and require more attention, especially in the "big data" era. Thus, supporting such applications with novel and efficient algorithms that a…
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…
Decentralised Moderation for Interoperable Social Networks: A Conversation-based Approach for Pleroma and the Fediverse
Vibhor Agarwal, Aravindh Raman, Nishanth Sastry +3
The recent development of decentralised and interoperable social networks (such as the "fediverse") creates new challenges for content moderators. This is because millions of posts…
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…
FairQ: Fair and Fast Rate Allocation in Data Centers
Ahmed M. Abdelmoniem, Brahim Bensaou
The peculiar congestion patterns in data centers are caused by the bursty and composite nature of traffic, the small bandwidth-delay product, and the tiny switch buffers. It is not…
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.…
Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach
Karan Pardeshi, Sukhpal Singh Gill, Ahmed M. Abdelmoniem
One of the most enticing research areas is the stock market, and projecting stock prices may help investors profit by making the best decisions at the correct time. Deep learning s…
Fortaleza: The emergence of a network hub
Eric Bragion, Habiba Akter, Mohit Kumar +3
Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected net…
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…
AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
Sundas Iftikhar, Sukhpal Singh Gill, Chenghao Song +15
Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature o…
The Switch from Conventional to SDN: The Case for Transport-Agnostic Congestion Control
Ahmed M. Abdelmoniem, Brahim Bensaou
To meet the timing requirements of interactive applications, the no-frills congestion-agnostic transport protocols like UDP are increasingly deployed side-by-side in the same netwo…
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…
Implementation and Evaluation of Data Center Congestion Controller with Switch Assistance
Ahmed M. Abdelmoniem, Brahim Bensaou
In this work, we provide the design and implementation of a switch-assisted congestion control algorithm for data center networks (DCNs). In particular, we provide a prototype of t…
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…