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Showing 1 to 20 of 44 for “"federated learning (FL)"”.
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Addressing stale gradients in asynchronous federated deep reinforcement learning
Advancements in reinforcement learning (RL) via deep neural networks have enabled their application to a variety of real-world problems. However, these applications often suffer from long training times. While attempts to distribute training have been successful in controlled scenarios, they face …
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A Practical Approach to Federated Learning
Machine learning models benefit from large and diverse training datasets. However, it is difficult for an individual organization to collect sufficiently diverse data. Additionally, the sensitivity of the data and government regulations such as GDPR, HIPPA, and CCPA restrict how organizations can …
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Practical Privacy-Preserving Federated Learning with Secure Multi-Party Computation
In a world with ever greater need for machine learning and artificial intelligence, it has be- come increasingly important to offload computation intensive tasks to companies with the compute resources to perform training on potentially sensitive data. In applications such as finance or healthcare, …
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Federated Learning for Secure Sensor Cloud
… to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a more reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure …
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Scaled: Scalable Federated Learning via Distributed Hash Table Based Overlays
… data in cloud centers for training Machine Learning (ML) models becomes unrealistic. To address this problem, Federated Learning (FL) is proposed. Yet, central bottleneck has become a severe concern since the central node in traditional FL is responsible for the communication and aggregation …
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P2P Based Personalized Federated Learning for Collaborative Model Sharing and Inferencing
Existing Federated Learning (FL) relies primarily on client-server architecture to train a single global model utilizing various local datasets. There are ongoing initiatives to improve the present state of federated learning. This method may not be suitable for clients with diverse requirements. …
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Privacy-aware Federated Learning with Global Differential Privacy
… training SNNs on large-scale distributed Machine Learning techniques like Federated Learning (FL). As federated learning involves many energy-constrained devices, there is a significant opportunity to take advantage of the energy efficiency offered by SNNs. However, it is necessary to address the …
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Images in motion?: a first look into video leakage in federated learning
Federated learning (FL) allows multiple entities to train a shared model collaboratively. Its core, privacy-preserving principle is that participants only exchange model updates, such as gradients, and never their raw, sensitive data. This approach is fundamental for applications in domains where …
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Privacy-Preserving and Robust Data Analytics under Distributed Settings
… within data processing and analytics workflows, especially in distributed systems, has become paramount. High-profile data breaches and privacy regulations like the General Data Protection Regulation (GDPR) underscore this urgency. Privacy-enhancing data release and learning paradigms such …
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Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks
Within the realm of federated learning (FL), adversarial entities can poison models, slowing down or destroying the FL training process. Therefore, attack prevention and mitigation are crucial for FL. Real-world scenarios may necessitate additional separation or abstraction between clients and …
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A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning
… the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent …
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Reinforcement Learning assisted Adaptive difficulty of Proof of Work (PoW) in Blockchain-enabled Federated Learning
… mining in various applications, such as Federated Learning (FL), which aims to train machine learning models across distributed devices collaboratively. The research objectives of this work involve developing novel RL-based techniques to address the heterogeneity problem in consortium …
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Caching techniques for reducing the communication cost of federated learning in IoT environments
Federated Learning (FL) introduces a new way to use data for machine learning, emphasizing privacy by handling data locally at the network's edge, instead of large, centralized servers. This thesis aims to improve FL on the Internet of Things (IoT) settings by developing and applying sophisticated …
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Energy and time efficient federated learning
… as the number of edge devices surges. Federated learning (FL) enables on-device training while preserving privacy, but edge devices typically operate under tight time and energy budgets, highlighting the need for time- and energy-efficient FL algorithms. While prior work focuses on …
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Breaking Privacy in Model-Heterogeneous Federated Learning
Federated learning (FL) is a communication protocol that allows multiple distrustful clients to collaboratively train a machine learning model. In FL, data never leaves client devices; instead, clients only share locally computed gradients or model parameters with a central server. As individual …
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Interpretability and Debugging for Distributed Privacy Preserving Machine Learning
Machine learning systems increasingly rely on privacy-preserving distributed training to leverage sensitive data across multiple organizations without centralization. Federated Learning (FL), a distributed privacy-preserving machine learning paradigm, enables hospitals, devices, and enterprises to …
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REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
Federated Learning (FL) is a sub-domain of machine learning (ML) that enforces privacy by allowing the user's local data to reside on their device. Instead of having users send their personal data to a server where the model resides, FL flips the paradigm and brings the model to the user's device …
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Federated Learning With Generalization To New Domains
Federated Learning (FL) is an area of research that focuses on training machine learning models in a decentralized fashion without having the need to store all data on one central server. In this thesis, we address the challenges of data heterogeneity and label scarcity in FL by proposing two novel …
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Multi-Center Federated Learning to Cluster Clients with non-IID data
Federated learning (FL) is a new machine learning paradigm to collaboratively learn an intelligent model across many clients without uploading local data to the server. Non-IID data across clients is a significant challenge for the FL system because its inherited distributed machine learning …
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Decentralized Machine Learning over Fragmented Data
… and algorithms to decentralize the machine learning pipeline. Current approaches in this area have centered around Federated Learning (FL), which enables model training over distributed data. However, FL’s dependence on central coordination and inflexibility with heterogeneous systems limit …
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