Federated Learning (FL) is a machine learning paradigm, which enables
multiple and decentralized clients to collaboratively train a model under the
orchestration of a central aggregator. Traditional FL solutions rely on the
trust assumption of the centralized aggregator, which forms cohorts of clients
in a fair and honest manner. However, a malicious aggregator, in reality, could
abandon and replace the client's training models, or launch Sybil attacks to
insert fake clients. Such malicious behaviors give the aggregator more power to
control clients in the FL setting and determine the final training results. In
this work, we introduce zkFL, which leverages zero-knowledge proofs (ZKPs) to
tackle the issue of a malicious aggregator during the training model
aggregation process. To guarantee the correct aggregation results, the
aggregator needs to provide a proof per round. The proof can demonstrate to the
clients that the aggregator executes the intended behavior faithfully. To
further reduce the verification cost of clients, we employ a blockchain to
handle the proof in a zero-knowledge way, where miners (i.e., the nodes
validating and maintaining the blockchain data) can verify the proof without
knowing the clients' local and aggregated models. The theoretical analysis and
empirical results show that zkFL can achieve better security and privacy than
traditional FL, without modifying the underlying FL network structure or
heavily compromising the training speed.

zkFL 使用零知识证明和区块链处理恶意聚合器问题，提高安全性和隐私性。

zkFL：基于零知识证明的联邦学习梯度聚合

zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated  Learning

Federated learning (FL) enhances data privacy with collaborative in-situ
training on decentralized clients. Nevertheless, FL encounters challenges due
to non-independent and identically distributed (non-i.i.d) data, leading to
potential performance degradation and hindered convergence. While prior studies
predominantly addressed the issue of skewed label distribution, our research
addresses a crucial yet frequently overlooked problem known as multi-domain FL.
In this scenario, clients' data originate from diverse domains with distinct
feature distributions, as opposed to label distributions. To address the
multi-domain problem in FL, we propose a novel method called Federated learning
Without normalizations (FedWon). FedWon draws inspiration from the observation
that batch normalization (BN) faces challenges in effectively modeling the
statistics of multiple domains, while alternative normalization techniques
possess their own limitations. In order to address these issues, FedWon
eliminates all normalizations in FL and reparameterizes convolution layers with
scaled weight standardization. Through comprehensive experimentation on four
datasets and four models, our results demonstrate that FedWon surpasses both
FedAvg and the current state-of-the-art method (FedBN) across all experimental
setups, achieving notable improvements of over 10% in certain domains.
Furthermore, FedWon is versatile for both cross-silo and cross-device FL,
exhibiting strong performance even with a batch size as small as 1, thereby
catering to resource-constrained devices. Additionally, FedWon effectively
tackles the challenge of skewed label distribution.

Federated learning（FL）通过在分散的客户端上进行协同原位训练，增强数据隐私性。然而，FL 面临由于非独立和同分布的（non-i.i.d）数据而引起的挑战，导致潜在的性能下降和收敛阻碍。本研究解决了一个被称为多域 FL 的关键问题，该问题经常被忽略，即客户数据来自具有不同特征分布的不同领域。为了解决 FL 中的多域问题，我们提出了一种称为无标准化联邦学习（FedWon）的新方法。FedWon 通过在 FL 中消除所有标准化，并用缩放的权重标准化重新参数化卷积层，以解决批量标准化面临的挑战。与 FedAvg 和当前最先进的方法（FedBN）相比，我们的结果表明 FedWon 在所有实验设置中均优于它们，某些域的改进超过 10％。此外，FedWon 即使在批量大小为 1 的情况下也能够以强劲的性能处理扭曲的标记分布，并且对于跨 silos 和跨设备的 FL 非常灵活。