Federated Learning (FL) systems evolve in heterogeneous and ever-evolving
environments that challenge their performance. Under real deployments, the
learning tasks of clients can also evolve with time, which calls for the
integration of methodologies such as Continual Learning. To enable research
reproducibility, we propose a set of experimental best practices that precisely
capture and emulate complex learning scenarios. Our framework, Freddie, is the
first entirely configurable framework for Federated Continual Learning (FCL),
and it can be seamlessly deployed on a large number of machines thanks to the
use of Kubernetes and containerization. We demonstrate the effectiveness of
Freddie on two use cases, (i) large-scale FL on CIFAR100 and (ii) heterogeneous
task sequence on FCL, which highlight unaddressed performance challenges in FCL
scenarios.

我们提出了一个可完全配置的框架 Freddie，用于解决分布式连续学习领域中存在的性能问题，该框架结合了实验最佳实践，能在大量机器上无缝部署，并通过两个使用案例验证了其有效性。

参数化联合持续学习与可复现研究

Parameterizing Federated Continual Learning for Reproducible Research

Federated Learning (FL) is an emerging paradigm that holds great promise for
privacy-preserving machine learning using distributed data. To enhance privacy,
FL can be combined with Differential Privacy (DP), which involves adding
Gaussian noise to the model weights. However, FL faces a significant challenge
in terms of large communication overhead when transmitting these model weights.
To address this issue, quantization is commonly employed. Nevertheless, the
presence of quantized Gaussian noise introduces complexities in understanding
privacy protection. This research paper investigates the impact of quantization
on privacy in FL systems. We examine the privacy guarantees of quantized
Gaussian mechanisms using R\'enyi Differential Privacy (RDP). By deriving the
privacy budget of quantized Gaussian mechanisms, we demonstrate that lower
quantization bit levels provide improved privacy protection. To validate our
theoretical findings, we employ Membership Inference Attacks (MIA), which gauge
the accuracy of privacy leakage. The numerical results align with our
theoretical analysis, confirming that quantization can indeed enhance privacy
protection. This study not only enhances our understanding of the correlation
between privacy and communication in FL but also underscores the advantages of
quantization in preserving privacy.

通过分析量化对联邦学习系统中的隐私保护的影响，本研究论文得出了量化位级越低，隐私保护越好的结论，并通过隶属推理攻击验证了这一理论分析，证实了量化确实可以增强隐私保护。

联邦学习中量化的效果：一种以 Rényi 差分隐私为视角的研究

The Effect of Quantization in Federated Learning: A Rényi Differential  Privacy Perspective

Fraudulent transactions and how to detect them remain a significant problem
for financial institutions around the world. The need for advanced fraud
detection systems to safeguard assets and maintain customer trust is paramount
for financial institutions, but some factors make the development of effective
and efficient fraud detection systems a challenge. One of such factors is the
fact that fraudulent transactions are rare and that many transaction datasets
are imbalanced; that is, there are fewer significant samples of fraudulent
transactions than legitimate ones. This data imbalance can affect the
performance or reliability of the fraud detection model. Moreover, due to the
data privacy laws that all financial institutions are subject to follow,
sharing customer data to facilitate a higher-performing centralized model is
impossible. Furthermore, the fraud detection technique should be transparent so
that it does not affect the user experience. Hence, this research introduces a
novel approach using Federated Learning (FL) and Explainable AI (XAI) to
address these challenges. FL enables financial institutions to collaboratively
train a model to detect fraudulent transactions without directly sharing
customer data, thereby preserving data privacy and confidentiality. Meanwhile,
the integration of XAI ensures that the predictions made by the model can be
understood and interpreted by human experts, adding a layer of transparency and
trust to the system. Experimental results, based on realistic transaction
datasets, reveal that the FL-based fraud detection system consistently
demonstrates high performance metrics. This study grounds FL's potential as an
effective and privacy-preserving tool in the fight against fraud.

研究介绍了使用联邦学习和可解释的人工智能的新方法来解决金融机构在欺诈检测方面面临的挑战，该方法能够在保护数据隐私的同时提高检测系统的性能，并具备透明性和可解释性。

透明和隐私：可解释人工智能和联邦学习在金融欺诈检测中的作用

Transparency and Privacy: The Role of Explainable AI and Federated  Learning in Financial Fraud Detection

Advanced researches on connected vehicles have recently targeted to the
integration of vehicle-to-everything (V2X) networks with Machine Learning (ML)
tools and distributed decision making. Federated learning (FL) is emerging as a
new paradigm to train machine learning (ML) models in distributed systems,
including vehicles in V2X networks. Rather than sharing and uploading the
training data to the server, the updating of model parameters (e.g., neural
networks' weights and biases) is applied by large populations of interconnected
vehicles, acting as local learners. Despite these benefits, the limitation of
existing approaches is the centralized optimization which relies on a server
for aggregation and fusion of local parameters, leading to the drawback of a
single point of failure and scaling issues for increasing V2X network size.
Meanwhile, in intelligent transport scenarios, data collected from onboard
sensors are redundant, which degrades the performance of aggregation. To tackle
these problems, we explore a novel idea of decentralized data processing and
introduce a federated learning framework for in-network vehicles,
C-DFL(Consensus based Decentralized Federated Learning), to tackle federated
learning on connected vehicles and improve learning quality. Extensive
simulations have been implemented to evaluate the performance of C-DFL, that
demonstrates C-DFL outperforms the performance of conventional methods in all
cases.

本研究提出一种基于协议的去中心化联邦学习框架 C-DFL，以应对联邦学习在车联网中的缺陷并提高学习效果，通过大量仿真实验证明其在所有情况下优于传统方法。