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    <title language="eng">Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments</title>
    <abstract language="eng">In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.</abstract>
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    <title language="eng">Security analysis of an Event Data Recorder system according to the HEAVENS model</title>
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      <first_name>Marco</first_name>
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    <title language="eng">Opportunities and challenges of cooperative intelligent transportation systems on accident analysis</title>
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    <title language="ces">Senzory pro automatizované řízení</title>
    <parentTitle language="ces">Automatizované řízení vozidel a autonomní doprava: Technické a humanitní perspektivy</parentTitle>
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      <first_name>David</first_name>
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      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
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      <first_name>Petr</first_name>
      <last_name>Zámečník</last_name>
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    <title language="ces">Automatizované řízení</title>
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    <title language="eng">Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions</title>
    <abstract language="eng">Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.</abstract>
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      <first_name>Yang</first_name>
      <last_name>Nan</last_name>
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      <first_name>Wim</first_name>
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      <first_name>Nina</first_name>
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      <first_name>Guang</first_name>
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    <title language="eng">A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers</title>
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    <title language="eng">Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning</title>
    <abstract language="eng">Peer incentivization (PI) is a recent approach, where all agents learn to reward or to penalize each other in a distributed fashion which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly integrated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information which limits scalability and applicability to real-world scenarios, where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to mutually exchange acknowledgment tokens to shape individual rewards. Each agent evaluates the monotonic improvement of its individual situation in order to accept or reject acknowledgment requests from other agents. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can defect from the protocol and where communication failures can occur. We also evaluate the sensitivity of MATE w.r.t. the choice of token values.</abstract>
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