93 research outputs found
Biomass-derived three-dimensional porous N-doped carbonaceous aerogel for efficient supercapacitor electrodes
Functionalized carbonaceous materials with hierarchical structure and developed porosity are highly desired in energy storage and conversion fields. In this work, a facile and scalable hydrothermal methodology was established to synthesise three-dimensional (3D) N-doped carbonaceous aerogels using biomass-based starting materials and polypyrrole as N-source. The effect of different calcination temperatures on the structural properties, type and content of N-species and electrochemical performance of the 3D N-doped carbonaceous aerogels were uncovered. Thanks to the combinatorial effect of the appropriate N content and porous structure, the obtained samples exhibited excellent electrochemical performance, in particular, an outstanding specific capacitance of 281.0 F g-1 achieved on the sample calcined at 600 °C. This methodology offers a new fabrication strategy to prepare nanoscale carbonaceous materials with desirable morphology and hierarchical architecture of great potentials for the applications in energy fields
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Federated Learning (FL) aims to train machine learning models for multiple
clients without sharing their own private data. Due to the heterogeneity of
clients' local data distribution, recent studies explore the personalized FL
that learns and deploys distinct local models with the help of auxiliary global
models. However, the clients can be heterogeneous in terms of not only local
data distribution, but also their computation and communication resources. The
capacity and efficiency of personalized models are restricted by the
lowest-resource clients, leading to sub-optimal performance and limited
practicality of personalized FL. To overcome these challenges, we propose a
novel approach named pFedGate for efficient personalized FL by adaptively and
efficiently learning sparse local models. With a lightweight trainable gating
layer, pFedGate enables clients to reach their full potential in model capacity
by generating different sparse models accounting for both the heterogeneous
data distributions and resource constraints. Meanwhile, the computation and
communication efficiency are both improved thanks to the adaptability between
the model sparsity and clients' resources. Further, we theoretically show that
the proposed pFedGate has superior complexity with guaranteed convergence and
generalization error. Extensive experiments show that pFedGate achieves
superior global accuracy, individual accuracy and efficiency simultaneously
over state-of-the-art methods. We also demonstrate that pFedGate performs
better than competitors in the novel clients participation and partial clients
participation scenarios, and can learn meaningful sparse local models adapted
to different data distributions.Comment: Accepted to ICML 202
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
In this work, besides improving prediction accuracy, we study whether
personalization could bring robustness benefits to backdoor attacks. We conduct
the first study of backdoor attacks in the pFL framework, testing 4 widely used
backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and
CIFAR-10, a total of 600 experiments. The study shows that pFL methods with
partial model-sharing can significantly boost robustness against backdoor
attacks. In contrast, pFL methods with full model-sharing do not show
robustness. To analyze the reasons for varying robustness performances, we
provide comprehensive ablation studies on different pFL methods. Based on our
findings, we further propose a lightweight defense method, Simple-Tuning, which
empirically improves defense performance against backdoor attacks. We believe
that our work could provide both guidance for pFL application in terms of its
robustness and offer valuable insights to design more robust FL methods in the
future. We open-source our code to establish the first benchmark for black-box
backdoor attacks in pFL:
https://github.com/alibaba/FederatedScope/tree/backdoor-bench.Comment: KDD 202
Towards understanding Android system vulnerabilities: Techniques and insights
National Research Foundation (NRF) Singapor
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