Federated Complex Qeury Answering
The paper introduces FedCQA, a method to answer complex queries on multi-source knowledge graphs (KGs) while preserving privacy. Current methods focus on single KGs and can’t be applied to multiple…
Continue readingContinual Federated Learning
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
The paper presents a framework for resource allocation in a mobile edge computing (MEC) system to provide customized virtual reality (VR) services to heterogeneous users. It introduces a quality of…
Continue readingA privacy-preserving, distributed and cooperative FCM-based learning approach for Cancer Research
The article presents a novel approach to Distributed Artificial Intelligence, using Federated Learning and Fuzzy Cognitive Maps (FCM) for cancer diagnosis. This method allows multiple participants to learn an FCM…
Continue readingFederated Learning Can Find Friends That Are Beneficial
The paper presents a new algorithm for Federated Learning (FL) that identifies beneficial collaborations among clients. The algorithm assigns adaptive aggregation weights to clients based on the conduciveness of their…
Continue readingPrivacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
The article discusses the development of a Secure-Aggregation (SecAgg) algorithm for residential short-term load forecasting, which maintains data privacy while also being robust against attacks. The algorithm minimizes communication complexity…
Continue readingLogit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures
The article focuses on distillation-based federated learning, a collaborative learning approach that reduces the risk of privacy invasion attacks and facilitates heterogeneous learning. The authors discuss the limitations of traditional…
Continue readingHow to Forget Clients in Federated Online Learning to Rank?
This study delves into the concept of ‘the right to be forgotten’ in line with data protection regulations like the GDPR. It focuses on Federated Online Learning to Rank (FOLTR)…
Continue readingA V2X-based Privacy Preserving Federated Measuring and Learning System
This paper proposes a system that utilizes Vehicle-to-Everything (V2X) technology for autonomous vehicles to exchange data and make real-time decisions. The system also operates a federated learning scheme to create…
Continue readingFLexChill: Improving Local Federated Learning Training with Logit Chilling
The article presents a new model training approach for federated learning called FLex&Chill, which utilizes the Logit Chilling method. This method addresses the challenges posed by data heterogeneity in federated…
Continue readingBrave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
This paper introduces Brave, a protocol that ensures Byzantine resilience and privacy-preserving property for Peer-to-Peer (P2P) Federated Learning (FL). In FL, multiple participants collaboratively train a global machine learning model…
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