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Found 19 papers, 6 papers with code
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator
For the distributed setting, we design a two-phase selection protocol: first, the aggregator selects the top clients based on our utility-driven ranking; then, a verifiable random function (VRF) ensures a BSA-resistant final selection.
Benchmarking Mutual Information-based Loss Functions in Federated Learning
Federated Learning (FL) has attracted considerable interest due to growing privacy concerns and regulations like the General Data Protection Regulation (GDPR), which stresses the importance of privacy-preserving and fair machine learning approaches.
Query-based Knowledge Transfer for Heterogeneous Learning Environments
Decentralized collaborative learning under data heterogeneity and privacy constraints has rapidly advanced.
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data
Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI).
FLStore: Efficient Federated Learning Storage for non-training workloads
With an aggregator server coordinating training, aggregating model updates, and storing metadata across rounds.
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
However, FL systems are vulnerable to attacks that are happening in malicious clients through data poisoning and model poisoning, which can deteriorate the performance of aggregated global model.
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledge from distributed clients by using their private and heterogeneous data.
Decentralised Moderation for Interoperable Social Networks: A Conversation-based Approach for Pleroma and the Fediverse
Recent work has exploited the conversational context of a post to improve this automatic tagging, e. g. using the replies to a post to help classify if it contains toxic speech.
Flashback: Understanding and Mitigating Forgetting in Federated Learning
In Federated Learning (FL), forgetting, or the loss of knowledge across rounds, hampers algorithm convergence, particularly in the presence of severe data heterogeneity among clients.
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms
This paper showcases two illustrative scenarios that highlight the potential of federated learning (FL) as a key to delivering efficient and privacy-preserving machine learning within IoT networks.
Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach
In this paper, our aim is to focus on the second aspect and build a model that predicts future prices with minimal errors.
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction
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
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 methods for training AI/ML models are becoming obsolete, especially with the growing concerns over privacy and security.
Towards Energy-Aware Federated Learning on Battery-Powered Clients
To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices.
Resource-Efficient Federated Learning
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication.
Rethinking gradient sparsification as total error minimization
Unlike with Top-$k$ sparsifier, we show that hard-threshold has the same asymptotic convergence and linear speedup property as SGD in the convex case and has no impact on the data-heterogeneity in the non-convex case.
On the Impact of Device and Behavioral Heterogeneity in Federated Learning
Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities.
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems
Many proposals exploit the compressibility of the gradients and propose lossy compression techniques to speed up the communication stage of distributed training.
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks.
