Currently, the federated graph neural network (GNN) has attracted a lot of
attention due to its wide applications in reality without violating the privacy
regulations. Among all the privacy-preserving technologies, the differential
privacy (DP) is the most promising one due to its effectiveness and light
computational overhead. However, the DP-based federated GNN has not been well
investigated, especially in the sub-graph-level setting, such as the scenario
of recommendation system. The biggest challenge is how to guarantee the privacy
and solve the non independent and identically distributed (non-IID) data in
federated GNN simultaneously. In this paper, we propose DP-FedRec, a DP-based
federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to
extend the local graph for each client, and thus solve the non-IID problem.
Most importantly, DP is applied not only on the weights but also on the edges
of the intersection graph from PSI to fully protect the privacy of clients. The
evaluation demonstrates DP-FedRec achieves better performance with the graph
extension and DP only introduces little computations overhead.

提出了 DP-FedRec，一种基于差分隐私的联邦图神经网络，应用了私有集交集（PSI）技术实现子图扩展，保护客户隐私并解决非独立同分布数据问题，在保证客户隐私的同时取得了更好的性能表现。

隐私保护的差分隐私子图级联图神经网络

A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy

Scalability and privacy are two critical concerns for cross-device federated
learning (FL) systems. In this work, we identify that synchronous FL -
synchronized aggregation of client updates in FL - cannot scale efficiently
beyond a few hundred clients training in parallel. It leads to diminishing
returns in model performance and training speed, analogous to large-batch
training. On the other hand, asynchronous aggregation of client updates in FL
(i.e., asynchronous FL) alleviates the scalability issue. However, aggregating
individual client updates is incompatible with Secure Aggregation, which could
result in an undesirable level of privacy for the system. To address these
concerns, we propose a novel buffered asynchronous aggregation method, FedBuff,
that is agnostic to the choice of optimizer, and combines the best properties
of synchronous and asynchronous FL. We empirically demonstrate that FedBuff is
3.3x more efficient than synchronous FL and up to 2.5x more efficient than
asynchronous FL, while being compatible with privacy-preserving technologies
such as Secure Aggregation and differential privacy. We provide theoretical
convergence guarantees in a smooth non-convex setting. Finally, we show that
under differentially private training, FedBuff can outperform FedAvgM at low
privacy settings and achieve the same utility for higher privacy settings.

本文提出了一种新的缓存异步聚合方法 FedBuff，它结合了同步和异步 federated learning 的最佳属性，可在不影响系统隐私的情况下提高效率，大大缓解了跨设备联合学习中的可扩展性和隐私问题。

带缓存异步聚合的联邦学习

Federated Learning with Buffered Asynchronous Aggregation

A common privacy issue in traditional machine learning is that data needs to
be disclosed for the training procedures. In situations with highly sensitive
data such as healthcare records, accessing this information is challenging and
often prohibited. Luckily, privacy-preserving technologies have been developed
to overcome this hurdle by distributing the computation of the training and
ensuring the data privacy to their owners. The distribution of the computation
to multiple participating entities introduces new privacy complications and
risks. In this paper, we present a privacy-preserving decentralised workflow
that facilitates trusted federated learning among participants. Our
proof-of-concept defines a trust framework instantiated using decentralised
identity technologies being developed under Hyperledger projects
Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued
from the appropriate authorities are able to establish secure, authenticated
communication channels authorised to participate in a federated learning
workflow related to mental health data.

本文介绍了一种基于分布式身份技术的隐私保护的分散工作流，用于促进参与者之间可信的联邦学习，限制只有获得适当机构发放的可验证凭证的实体才能建立安全、经过认证的通信渠道，参与与心理健康有关的联邦学习流程。