Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
Department of Electronic Systems, Norwegian University of Science and Technology（挪威科学技术大学电子系统系）；CSIRO’s Data61（澳大利亚数据61机构）；Department of Information and Communications Engineering, Aalto University（艾尔沃斯大学信息与通信工程系）
Ehsan Lari, Reza Arablouei, Stefan Werner
备注：
5 pages, 4 figures, Accepted for presentation at the 34th European Signal Processing Conference (EUSIPCO 2026) in Bruges, Belgium
We propose a Byzantine-resilient federated conformal prediction (FCP) method that leverages partial model sharing, where only a subset of model parameters is exchanged each round. Unlike existing robust FCP approaches that primarily harden the calibration stage, our method protects both the federated training and conformal calibration phases. During training, partial sharing inherently restricts the attack surface and attenuates poisoned updates while reducing communication. During calibration, clients compress their non-conformity scores into histogram-based characterization vectors, enabling the server to detect Byzantine clients via distance-based maliciousness scores and to estimate the conformal quantile using only benign contributors. Experiments across diverse Byzantine attack scenarios show that the proposed method achieves closer-to-nominal coverage with substantially tighter prediction intervals than standard FCP, establishing a robust and communication-efficient approach to federated uncertainty quantification.
Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
Department of Electronic Systems, Norwegian University of Science and Technology（电子系统系，挪威科学技术大学）；Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）；Department of Information and Communications Engineering, Aalto University（信息与通信工程系，艾尔托大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle’s Illinois College of Medicine（卡尔伊利诺伊医学院）；GE Healthcare（GE医疗）；University of Chicago（芝加哥大学）
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both differential privacy (DP) and $(α,f)$-Byzantine resilience. To the best of our knowledge, this is the first work to tackle this problem from a theoretical point of view. A key finding of our analyses is that the classical approaches to these two (seemingly) orthogonal issues are incompatible. More precisely, we show that a direct composition of t…
Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education（嵌入式系统与服务计算教育部重点实验室（同济大学））
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, which will cause the global model to be manipulated by the attacker or fail to converge. On non-iid data, the current methods are not effective in defensing against Byzantine attacks. In this paper, we propose a Byzantine-robust framework for federated learning via credibility assessment on non-iid data (BRCA). Credibility assessment is designed to d…
We present Garfield, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine-resilient. Garfield relies on a novel object-oriented design, reducing the coding effort, and addressing the vulnerability of the shared-graph architecture followed by classical ML frameworks. Garfield encompasses various communication patterns and supports computations on CPUs and GPUs, allowing addressing the general question of the very practical cost of Byzantine resilience in SGD-based ML applications. We re…
Uppsala University（乌普萨拉大学）；The University of Hong Kong（香港大学）；RISE, the Research Institutes of Sweden（瑞典研究院（RISE））
Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants may upload arbitrary local updates to the central server to degrade the performance of the global model. In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning. These solutions were claimed to be Byzantine-robust, under certain assumptions. Other than that, new attack strategies are emerging, striving to circumvent the defense schem…
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial edge devices. While existing works on FL mitigate such compromised devices by only aggregating a subset of the local models at the server side, they still cannot successfully ignore the outliers due to imprecise scoring rule. In this paper, we propose an effective Byzantine-robust FL framework, namely dummy contrastive aggregation, by defining a novel scoring function that sensitively discriminates whether the model ha…
Federated learning (FL) has recently become a hot research topic, in which Byzantine robustness, communication efficiency and privacy preservation are three important aspects. However, the tension among these three aspects makes it hard to simultaneously take all of them into account. In view of this challenge, we theoretically analyze the conditions that a communication compression method should satisfy to be compatible with existing Byzantine-robust methods and privacy-preserving methods. Motivated by the analysis results, we propose a novel communication compression method called consensus …
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model. Standard federated learning algorithms applied to this setting are vulnerable to Byzantine attacks on even a small fraction of agents. We propose a novel algorithm with a robust aggregation oracle that utilizes the geometric median. We prove that our proposed algorithm is robust to Byzantine attacks on fewer than half of agents and achieves a sublinear $\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqr…
We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses. We benchmark against competing protocols and show the empirical superiority of the proposed protocols. Finally, we remark that our protocols with bucketing can be naturally combined with privacy-guaranteeing procedures to introduce security against a semi-honest server. The code for evaluation is provided in https://github.…
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model
Key Laboratory of Beijing Network Technology, Beihang University（北京网络技术重点实验室，北京航空航天大学）；Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing, Guangxi Normal University（广西多源信息整合与智能处理协同创新中心，广西师范大学）；School of Computer Science and Technology, Zhengzhou University of Light Industry（轻工业学院计算机科学与技术学院）；School of Cyber Science and Technology, Beihang University（网络安全科学与技术学院，北京航空航天大学）；Shanghai Key Laboratory of Computer Software Evaluating and Testing（上海计算机软件评测测试重点实验室）
University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）
Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a global model using their local datasets. Each user then masks its local model via random keys, and the masked models are aggregated at a central server to compute the global model for the next iteration. As the local models are protected by random masks, the server cannot observe their true values. This presents a major challenge for the resilience o…
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federated Averaging, a novel algorithm for robust federated learning that is designed to detect failures, attacks, and bad updates provided by participants in a collaborative model. We propose a Hidden Markov Model to model and learn the quality of model updates provided by each partic…
Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）
Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning attacks that can degrade their performance. In this paper, we develop a novel federated recommendation technique that is robust against the poisoning attack where Byzantine clients prevail. We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients. We then propose a robust learning strategy where instead of using model parameters, the central server computes and utilizes…
The Ohio State University（俄亥俄州立大学）；Duke University（杜克大学）；Air Force Research Laboratory（空军研究实验室）
Federated learning (FL) is an emerging machine learning paradigm, in which clients jointly learn a model with the help of a cloud server. A fundamental challenge of FL is that the clients are often heterogeneous, e.g., they have different computing powers, and thus the clients may send model updates to the server with substantially different delays. Asynchronous FL aims to address this challenge by enabling the server to update the model once any client's model update reaches it without waiting for other clients' model updates. However, like synchronous FL, asynchronous FL is also vulnerable t…
Byzantine-resilient federated online learning for Gaussian process regression
School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802 USA（电气工程与计算机科学学院，宾夕法尼亚州立大学，大学公园，PA 16802 USA）；Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061 USA（弗吉尼亚理工交通研究所，弗吉尼亚理工学院与州立大学，布莱克堡，VA 24061 USA）
Sun Yat-Sen University（中山大学）；Pazhou Lab（琶洲实验室）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
We consider the federated learning problem where data on workers are not independent and identically distributed (i.i.d.). During the learning process, an unknown number of Byzantine workers may send malicious messages to the central node, leading to remarkable learning error. Most of the Byzantine-robust methods address this issue by using robust aggregation rules to aggregate the received messages, but rely on the assumption that all the regular workers have i.i.d. data, which is not the case in many federated learning applications. In light of the significance of reducing stochastic gradien…
The Blavatnik School of Computer Science（布拉瓦尼克计算机科学学院）；Tel Aviv University（特拉维夫大学）
This work presents a new distributed Byzantine tolerant federated learning algorithm, HoldOut SGD, for Stochastic Gradient Descent (SGD) optimization. HoldOut SGD uses the well known machine learning technique of holdout estimation, in a distributed fashion, in order to select parameter updates that are likely to lead to models with low loss values. This makes it more effective at discarding Byzantine workers inputs than existing methods that eliminate outliers in the parameter-space of the learned model. HoldOut SGD first randomly selects a set of workers that use their private data in order …
Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzatine poisoning adversarial attacks. We argue that the federated learning model has to avoid those kind of adversarial attacks through filtering out the adversarial clients by means of the federated aggregation operator. We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model. We assess it as a defense against adversarial attacks …
We consider the problem of Byzantine fault-tolerance in federated machine learning. In this problem, the system comprises multiple agents each with local data, and a trusted centralized coordinator. In fault-free setting, the agents collaborate with the coordinator to find a minimizer of the aggregate of their local cost functions defined over their local data. We consider a scenario where some agents ($f$ out of $N$) are Byzantine faulty. Such agents need not follow a prescribed algorithm correctly, and may communicate arbitrary incorrect information to the coordinator. In the presence of Byz…
The Ohio State University（俄亥俄州立大学）；Duke University（杜克大学）
In federated learning, multiple client devices jointly learn a machine learning model: each client device maintains a local model for its local training dataset, while a master device maintains a global model via aggregating the local models from the client devices. The machine learning community recently proposed several federated learning methods that were claimed to be robust against Byzantine failures (e.g., system failures, adversarial manipulations) of certain client devices. In this work, we perform the first systematic study on local model poisoning attacks to federated learning. We as…
Federated learning (FL) is a privacy-friendly type of machine learning where devices locally train a model on their private data and typically communicate model updates with a server. In decentralized FL (DFL), peers communicate model updates with each other instead. However, DFL is challenging since (1) the training data possessed by different peers is often non-i.i.d. (i.e., distributed differently between the peers) and (2) malicious, or Byzantine, attackers can share arbitrary model updates with other peers to subvert the training process. We address these two challenges and present Bristl…
The problem of saddle-point avoidance for non-convex optimization is quite challenging in large scale distributed learning frameworks, such as Federated Learning, especially in the presence of Byzantine workers. The celebrated cubic-regularized Newton method of \cite{nest} is one of the most elegant ways to avoid saddle-points in the standard centralized (non-distributed) setup. In this paper, we extend the cubic-regularized Newton method to a distributed framework and simultaneously address several practical challenges like communication bottleneck and Byzantine attacks. Note that the issue o…
