Secure Federated Research Articles

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.

Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm

The trend of the next generation of the internet has already been scrutinized by top analytics enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the global population will have their personal data covered under privacy regulations. This alarming statistic necessitates the orchestration of several security components to address the enormous challenges posed by federated and distributed learning environments. Federated learning (FL) is a promising technique that allows multiple parties to collaboratively train a model without sharing their data. However, even though FL is seen as a privacy-preserving distributed machine learning method, recent works have demonstrated that FL is vulnerable to some privacy attacks. Homomorphic encryption (HE) and differential privacy (DP) are two promising techniques that can be used to address these privacy concerns. HE allows secure computations on encrypted data, while DP provides strong privacy guarantees by adding noise to the data. This paper first presents consistent attacks on privacy in federated learning and then provides an overview of HE and DP techniques for secure federated learning in next-generation internet applications. It discusses the strengths and weaknesses of these techniques in different settings as described in the literature, with a particular focus on the trade-off between privacy and convergence, as well as the computation overheads involved. The objective of this paper is to analyze the challenges associated with each technique and identify potential opportunities and solutions for designing a more robust, privacy-preserving federated learning framework.

A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning

Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals' wish to “hide in the crowd” for privacy frequently clashes with aggregator' need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.

SecFL – Secure Federated Learning Framework for predicting defects in sheet metal forming under variability

With the ongoing digitization of the manufacturing industry and the ability to bring together data from specific manufacturing processes, there is enormous potential to use machine learning (ML) techniques to improve such processes. In this context, the competitive automotive industry can take advantage of the ML power by predicting defects before they occur, aiming to reduce the scrap rate and increase the robustness and reliability of the production processes. In a real world scenario, small and medium size companies do not have the amount of data the big companies have, which can prevent the usage of ML models in this vital niche for the industry. A collaboration in terms of data usage to develop powerful and general industry solutions is hindered by data privacy concerns despite similar problems. This paper addresses these concerns by providing a framework based on the Federated Learning (FL) method combined with Digital Envelopes (DE) to allow the ML models training while keeping the data of the partners and the models parameters private and protected against external cyber-attacks, which is one of the weaknesses of FL as of now. A case study was carried out to demonstrate the effectiveness of the proposed framework on handling data poisoning attacks to the training data and also the models’ weights.

Distributed and Secure Federated Learning for Wireless Computing Power Networks

The explosively growth of mobile applications imposes much burden on the current computing networks. Wireless Computing Power Network (WCPN), as an emerging computing architecture, can sense and coordinate computing resources through agile wireless communications, and realize distributed intelligence based on federated learning. However, the mobility and heterogeneity of WCPN nodes typically impact the security (e.g., malicious node disturbance) and efficiency of federated learning in WCPN. In light of this, this paper proposes a provable secure and decentralized federated learning based on blockchain for WCPN, where nodes can freely participate or leave the WCPN federated training without authorization and security threats. Particularly, we design a blockchain with proof-of-accuracy (PoAcc) consensus scheme to deeply integrate with the federated learning procedure, in which high-accuracy local models have the priority of aggregation, thus accelerating the convergence of federated learning and improving the efficiency of WCPN. The proposed PoAcc is proved to be secure as long as the ratio of honest to malicious nodes is above a lower bound. To further meet the security requirement of PoAcc, we then propose an evolutionary game-based incentive scheme that incentivizes honest nodes to participate the WCPN federated learning under malicious node disturbance. Numerical results show that the proposed scheme ensures the consistency and security of federated learning in WCPN, while outperforming the benchmarks in terms of model accuracy and resource consumption.

S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow Scheduling

Federated learning has been proposed as a promising distributed machine learning paradigm with strong privacy protection on training data. In this paper, we study federated graph learning (FGL) under a cross-silo setting where several servers are connected by a wide-area network and cooperate in training a graph convolutional network (GCN) model. We find that communication becomes the main system bottleneck because of frequent information exchanges among federated severs and limited network bandwidth. To tackle this challenge, we design S-Glint, a secure federated graph learning system, containing a homomorphic encryption-based protocol with pre-aggregation and batching strategies. The network traffic throttling and flows scheduling are also proposed to accelerate the learning process. To evaluate the effectiveness of S-Glint, we conduct experiments using trace-driven simulations. The results show that S-Glint can significantly outperform existing federated learning solutions.

Efficient and Secure Federated Learning for Financial Applications

Conventional machine learning (ML) and deep learning approaches require sharing customers’ sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a significant challenge for financial companies. To address this challenge, federated learning has emerged as a promising approach to protect data privacy. However, the high communication costs associated with federated systems, particularly for large neural networks, can be a bottleneck. To mitigate this issue, it is necessary to limit the number and size of communications for practical training of large neural structures. Gradient sparsification is a technique that has gained increasing attention as a method to reduce communication costs, as it updates only significant gradients and accumulates insignificant gradients locally. However, the secure aggregation framework cannot directly employ gradient sparsification. To overcome this limitation, this article proposes two sparsification methods for reducing the communication costs of federated learning. The first method is a time-varying hierarchical sparsification method for model parameter updates, which addresses the challenge of maintaining model accuracy after a high sparsity ratio. This method can significantly reduce the cost of a single communication. The second method is to apply sparsification to the secure aggregation framework. Specifically, the encryption mask matrix is sparsified to reduce communication costs while protecting privacy. Experiments demonstrate that our method can reduce the upload communication costs to approximately 2.9% to 18.9% of the conventional federated learning algorithm under different non-IID experiment settings when the sparsity rate is 0.01.

Improved Design and Application of Security Federation Algorithm

(1) Background: To avoid affecting the accuracy and practicability of the security federation model due to the geographical and environmental factors involved in each local model and set the corresponding weights for each local model, the local model parameters and weights participated in the calculation at the same time. (2) Methods: Apply the improved model to the income evaluation of taxi drivers. Multiple linear regression was used to fit the local model parameters, and the loss function value was calculated. Then, according to the improved security federation algorithm, the model parameters and local model weights were encrypted by using the Paillier homomorphic encryption algorithm, and the encrypted model parameter information was uploaded to the aggregation server for aggregation average. (3) Results: The experimental results show that after 1000 iterations, the accuracy curve converges in the interval [0.93, 0.97]; the mean accuracy value was 94.27%, and the mean loss function value was 1.0886. It was the same understanding that the mean value of the loss function calculated by the traditional model was 1.9910. (4) From the model and data, the accuracy of the improved model has been improved. It can better reflect the income of taxi drivers.

Secure Federated Learning for Cognitive Radio Sensing

This paper considers reliable and secure Spectrum Sensing (SS) based on Federated Learning (FL) in the Cognitive Radio (CR) environment. Motivation, architectures, and algorithms of FL in SS are discussed. Security and privacy threats on these algorithms are overviewed, along with possible countermeasures to such attacks. Some illustrative examples are also provided, with design recommendations for FL-based SS in future CRs.

Efficient and Secure Federated Learning With Verifiable Weighted Average Aggregation

Federated learning allows a large number of participants to collaboratively train a global model without sharing participant's local data. Participants train local models with their local data and send gradients to the cloud server for aggregation. Unfortunately, as a third party, the cloud server cannot be fully trusted. Existing research has shown that a compromised cloud server can extract sensitive information of participant's local data from gradients. In addition, it can even forge the aggregation result to corrupt the global model without being detected. Therefore, in a secure federated learning system, both the privacy and aggregation correctness of the uploaded gradients should be guaranteed. In this article, we propose a secure and efficient federated learning scheme with verifiable weighted average aggregation. By adopting the masking technique to encrypt both weighted gradients and data size, our scheme can support the privacy-preserving weighted average aggregation of gradients. Moreover, we design the verifiable aggregation tag and propose an efficient verification method to validate the weighted average aggregation result, which greatly improves the performance of the aggregation verification. Security analysis shows that our scheme is provably secure. Extensive experiments demonstrate the efficiency of our scheme compared with the state-of-the-art approaches.

Optimized Secure Federated Learning for Event Detection in Big Data using Blockchain Mechanism

Currently, cloud storage in blockchain and federated learning technology provides better security among data transmission and file access. But, in some of the cases, security issues arose. So, to avoid security problems and offer better protection in a cloud environment, a novel optimized buffalo-based Homomorphic SHA blockchain (OBHSB). In this model for accessing the cloud storage data with the key matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. However, the parameters in the proposed model were compared with the existing models to make sure better performance was attained through the proposed model. Attack was considered as an event in this research. In the performance analysis, the performance rate of the proposed model was validated. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design; model performs hashing, encryption, decryption, and key matching mechanisms. The results proposed model is observed to have 100% confidentiality rate after attack.

LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing

Nowadays, many edge computing service providers expect to leverage the computational power and data of edge nodes to improve their models without transmitting data. Federated learning facilitates collaborative training of global models among distributed edge nodes without sharing their training data. Unfortunately, existing privacy-preserving federated learning applied to this scenario still faces three challenges: 1) It typically employs complex cryptographic algorithms, which results in excessive training overhead; 2) It cannot guarantee Byzantine robustness while preserving data privacy; and 3) Edge nodes have limited computing power and may drop out frequently. As a result, the privacy-preserving federated learning cannot be effectively applied to edge computing scenarios. Therefore, we propose a lightweight and secure federated learning scheme LSFL, which combines the features of privacy-preserving and Byzantine-Robustness. Specifically, we design the Lightweight Two-Server Secure Aggregation protocol, which utilizes two servers to enable secure Byzantine robustness and model aggregation. This scheme protects data privacy and prevents Byzantine nodes from influencing model aggregation. We implement and evaluate LSFL in a LAN environment, and the experiment results show that LSFL meets fidelity, security, and efficiency design goals, and maintains model accuracy compared to the popular FedAvg scheme.

Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified.

Pocket Diagnosis: Secure Federated Learning Against Poisoning Attack in the Cloud

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this article, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> . Concretely, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to resist DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> as well as its capability of resisting DI-level poisoning attacks.

Toward Secure Federated Learning for IoT Using DRL-Enabled Reputation Mechanism

Federated learning (FL) has emerged to leverage datasets from multiple devices to improve the performance of a machine learning (ML) model while providing privacy preservation for devices. The training data is collected at the devices, also known as FL workers, which collaboratively train a global learning model and share their local model updates with a central entity or server without sharing their data. However, FL can be susceptible to various adversarial attacks that target its security and privacy. In particular, the workers can upload unreliable local model updates, leading to corruption of the main FL task. Workers may intentionally contribute unreliable local updates by launching poisoning attacks or unintentionally by updating low-quality models caused by high device mobility, limited device resources, or unstable network connection. Consequently, identifying reliable and trustworthy workers becomes critical for FL security. In this article, the concept of reputation is adopted as a metric to evaluate workers’ reliability and trustworthiness. In addition, deep reinforcement learning (DRL)-based reputation mechanism is proposed for optimal selection and evaluation of reliable FL workers. Due to the dynamic nature of worker behavior in the FL environment, the DRL-based algorithm deep deterministic policy gradient (DDPG) is employed to improve the FL model accuracy and stability. We compare the performance of our proposed method with a conventional reputation method and deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -networks (DQNs)-based reputation method. Our simulation results demonstrate that our proposed method can improve FL accuracy by more than 30% under various scenarios and achieves better convergence than the other methods.

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data.

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.

A Redactable Blockchain Framework for Secure Federated Learning in Industrial Internet of Things

Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because model updates are easily tampered with by malicious agents. Motivated by this observation, we propose a novel chameleon hash scheme with a changeable trapdoor (CHCT) for secure FL in IIoT settings. Our scheme imposes various constraints on the use of trapdoor. We give a rigorous security analysis on our CHCT scheme. We also instantiate the CHCT scheme as a redactable medical blockchain (RMB). The experimental evaluations demonstrate the practical utility of CHCT in terms of accuracy and efficiency.

SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits

The multi-armed bandit is a reinforcement learning model where a learning agent repeatedly chooses an action (pull a bandit arm) and the environment responds with a stochastic outcome (reward) coming from an unknown distribution associated with the chosen arm. Bandits have a wide-range of application such as Web recommendation systems. We address the cumulative reward maximization problem in a secure federated learning setting, where multiple data owners keep their data stored locally and collaborate under the coordination of a central orchestration server. We rely on cryptographic schemes and propose Samba, a generic framework for Secure federAted Multi-armed BAndits. Each data owner has data associated to a bandit arm and the bandit algorithm has to sequentially select which data owner is solicited at each time step. We instantiate Samba for five bandit algorithms. We show that Samba returns the same cumulative reward as the nonsecure versions of bandit algorithms, while satisfying formally proven security properties. We also show that the overhead due to cryptographic primitives is linear in the size of the input, which is confirmed by our proof-of-concept implementation.

FLCert: Provably Secure Federated Learning Against Poisoning Attacks

Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malicious clients poison the training process via manipulating their local training data and/or local model updates sent to the cloud server, such that the poisoned global model misclassifies many indiscriminate test inputs or attacker-chosen ones. Existing defenses mainly leverage Byzantine-robust federated learning methods or detect malicious clients. However, these defenses do not have provable security guarantees against poisoning attacks and may be vulnerable to more advanced attacks. In this work, we aim to bridge the gap by proposing FLCert, an ensemble federated learning framework, that is provably secure against poisoning attacks with a bounded number of malicious clients. Our key idea is to divide the clients into groups, learn a global model for each group of clients using any existing federated learning method, and take a majority vote among the global models to classify a test input. Specifically, we consider two methods to group the clients and propose two variants of FLCert correspondingly, i.e., FLCert-P that randomly samples clients in each group, and FLCert-D that divides clients to disjoint groups deterministically. Our extensive experiments on multiple datasets show that the label predicted by our FLCert for a test input is provably unaffected by a bounded number of malicious clients, no matter what poisoning attacks they use.

Permissioned Blockchain Frame for Secure Federated Learning

Federated learning is an emerging technology that solves the privacy problem of training data in multi-party machine learning. However, this technology is vulnerable to a series of system security problems. In this letter, we leverage Hyperledger Fabric permissioned blockchain architecture to build a secure and reliable federated learning platform across multiple data owners, where individual local updates are encrypted based on threshold homomorphic encryption and then recorded on a distributed ledger. The security analysis shows that our solution can effectively deal with the existing privacy and security issues in the federated learning system. The numerical results show that the scheme is feasible and efficient.