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Found 16 papers, 2 papers with code
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
Federated Learning (FL) is a pivotal approach in decentralized machine learning, especially when data privacy is crucial and direct data sharing is impractical.
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher
Federated Learning (FL) promises better privacy guarantees for individuals' data when machine learning models are collaboratively trained.
Robustness of Decentralised Learning to Nodes and Data Disruption
Through these configurations, we are able to show the non-trivial interplay between the properties of the network connecting nodes, the persistence of knowledge acquired collectively before disruption or lack thereof, and the effect of data availability pre- and post-disruption.
Initialisation and Topology Effects in Decentralised Federated Learning
Fully decentralised federated learning enables collaborative training of individual machine learning models on distributed devices on a communication network while keeping the training data localised.
Impact of network topology on the performance of Decentralized Federated Learning
We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation.
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation.
Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios
Thus, fully decentralized learning can help in this case.
The effect of network topologies on fully decentralized learning: a preliminary investigation
Specifically, we highlight the different roles in this process of more or less connected nodes (hubs and leaves), as well as that of macroscopic network properties (primarily, degree distribution and modularity).
Anomaly Detection through Unsupervised Federated Learning
Experiments show that our method is robust, and it can detect communities consistent with the ideal partitioning in which groups of clients having the same inlier patterns are known.
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks
The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning
We show that our system based on Reinforcement Learning is able to automatically learn a very efficient strategy to reduce the traffic on the cellular network, without relying on any additional context information about the opportunistic network.
A communication efficient distributed learning framework for smart environments
In the specific case analysed in the paper, we focus on a learning task, considering two distributed learning algorithms.
Energy efficient distributed analytics at the edge of the network for IoT environments
We analyse the performance of different configurations of the distributed learning framework, in terms of (i) accuracy obtained in the learning task and (ii) energy spent to send data between the involved nodes.
Federated Feature Selection for Cyber-Physical Systems of Systems
In this work, we address such a problem by proposing a federated feature selection algorithm where all the AVs collaborate to filter out, iteratively, the redundant or irrelevant attributes in a distributed manner, without any exchange of raw data.
Optimising cost vs accuracy of decentralised analytics in fog computing environments
Decentralising AI tasks on several cooperative devices means identifying the optimal set of locations or Collection Points (CP for short) to use, in the continuum between full centralisation (i. e., all data on a single device) and full decentralisation (i. e., data on source locations).
Dynamic Hard Pruning of Neural Networks at the Edge of the Internet
Results show that DynHP compresses a NN up to $10$ times without significant performance drops (up to $3. 5\%$ additional error w. r. t.
