Federated learning (FL) allows the collaborative training of a model while keeping data decentralized. However, FL has been shown to be vulnerable to poisoning attacks. Model poisoning, in particular, enables adversaries to manipulate their local updates, leading to a significant degradation of the global model accuracy. Most state-of-the-art attacks rely on prior knowledge of the server's aggregation rules (AGRs), so-called AGR-tailored attacks, making them more effective than AGR-agnostic attacks that lack such information. In this paper, we propose AIDA (Adaptive Inference-Driven Attack), the first adaptive AGR-agnostic attack. This attack begins with an AGR-agnostic approach where it analyzes the training process to identify the aggregation rule that the server is using, and it then transitions to a more powerful AGR-tailored attack. Extensive experiments reveal that AIDA substantially outperforms other state-of-the-art AGR-agnostic attacks, achieving additional model degradation of up to 3.01% on Krum, 13.96% on MKrum, and 2.85% on FLAME. These results demonstrate the effectiveness of AIDA and its ability to significantly degrade the global model performance, making it a more generic and powerful attack than existing AGR-agnostic approaches.

Federated Learning (FL) is a distributed machine learning approach that enhances data privacy by training models across multiple devices or servers without centralizing raw data. Traditional FL frameworks, which rely on synchronous updates and homogeneous resources, face significant performance challenges in real-world deployments. These challenges include handling heterogeneous data distributions (non-IID) and varying computational resources across clients, leading to performance degradation. To address these issues, asynchronous FL frameworks have been proposed, but they introduce new complexities such as communication latency and workload management across geo-distributed servers.

This paper focuses on the impact of network latency and non-IID data distributions on asynchronous multi-server FL systems. We propose and evaluate three methods to mitigate the adverse effects of data heterogeneity on model accuracy: (1) transferring clients between servers; (2) sharing clients among servers so that they alternatively train their models; and (3) sharing clients among servers with model averaging. Our contributions include a reproducible experimental framework for multi-server FL, strategies for optimizing client-server interactions, and an analysis of the effectiveness of these strategies in reducing the impact of non-IID data distributions. Experimental results demonstrate that our methods can reduce training time by up to 85.67%, decrease the number of updates required by 85.82%, and improve accuracy by 4.815% in heterogeneous environments, compared to Spyker, a state-of-the-art multi-server FL algorithm.

This thesis explores the application of a modular execution environment, specifically utilizing the Move Virtual Machine (MoveVM), within a blockchain-agnostic framework. The study aims to demonstrate how this modular approach can enhance the execution capability of existing blockchain systems. The case study focuses on the IOTA Distributed Ledger Technology (DLT), known for its unique Tangle architecture, which differentiates it from traditional blockchain technologies.

The research begins by detailing the current limitations in existing blockchain platforms, such as their dependence on specific consensus algorithms and rigid execution environments. It then introduces the MoveVM, developed firstly by the Libra (Diem) project and now by the projects Sui and Aptos, highlighting its advantages in terms of security, programmability, and modularity.

By integrating an object-flavored MoveVM into the IOTA framework, the study examines how the modular smart contract execution environment can operate almost independently of the underlying ledger. The work for this thesis was conducted in two main parts. In the first part, a prototype was created by integrating a modified version of the existing IOTA node software with an object-flavored Move execution environment. In the second part, a Move Swap smart contract was developed at the application layer to showcase the system's ability to support an advanced intent-based architecture. This approach, which executes based on user intents rather than declarative smart contract commands, offers significant economic benefits and reduces unnecessary costs for users.

To validate the effectiveness of this approach, the thesis presents empirical data and performance metrics gathered from various test scenarios. The results demonstrate the successful integration of the Move smart contract execution into the IOTA node software, but also significant improvements resource efficiency thanks to the intent-based architecture.

In conclusion, this thesis contributes to the growing body of knowledge on blockchain technology by showcasing the potential of MoveVM in enhancing the functionality and performance of blockchain-agnostic networks. Moreover, the findings suggest that the intent-based approach not only simplifies user interactions but also enables advanced functionalities and custom on-chain VMs, paving the way for innovative applications in DLTs.

Distributed consensus algorithms are essential for maintaining data reliability and consistency across computer networks, ensuring that all nodes agree to a single state despite failures or malicious disruptions. Despite the critical role of Byzantine Fault Tolerant State Machine Replication (BFT SMR) protocols in securing such systems, existing protocols like Practical Byzantine Fault Tolerance (PBFT) and HotStuff still struggle with scalability, particularly in large applications where latency and throughput are crucial. A notable tradeoff is that, for example, PBFT is able to utilize network resources very well, but has significant communication complexity for reaching consensus, especially in scenarios where leaders are faulty. HotStuff manages to reduce the communication complexity by streamlining the leader-change mechanism, but by doing so is unable to fully utilise network resources in most cases.

This thesis introduces Para-HotStuff, a new BFT SMR protocol designed to improve the performance of streamlined leader-based consensus protocols by enabling parallel processing of blocks. Para-HotStuff builds on the core functionality of HotStuff, maintaining its linear communication complexity while incorporating parallel voting features from PBFT to optimize resource utilization and network bandwidth. The performance of Para-HotStuff was evaluated and compared to HotStuff under various conditions and protocol configurations, showing improved throughput and latency, particularly in scenarios with high transaction loads. Further research could explore implementing further mechanisms to improve \name{} as well as doing more comprehensive evaluations on how it behaves in real-world settings.

Federated Learning (FL) systems often suffer from high variability in the final model due to inconsistent training across distributed clients. This paper identifies the problem of high variance in models trained through FL and proposes a novel approach to mitigate this issue through scheduling simulations subject to precedence constraints. By effectively scheduling the execution of client tasks and parameter server updates, we aim to reduce the variance in the final aggregated model. Through a series of experiments, we demonstrate that our proposed scheduling method significantly reduces model variance, while not impacting the time of simulation drastically. Additionally, we propose 2 algorithms to solve the problem of scheduling under precedence constraints - Ant Colony Optimisation, and an Evolutionary Algorithm - to minimize the makespan of simulations.

Streamlined Byzantine Fault Tolerance (BFT) protocols have been developed to create efficient view-changes. To improve upon their performance, trusted components have been introduced to prevent equivocation within a protocol. However, these trusted components suffer from rollback attacks. We identify clear examples of rollback attacks on Damysus, and design a novel method to mitigate the attack vector. This is done via a pacemaker protocol called Artemis, which allocates trusted components to epochs. Integrating Artemis with Damysus creates Apollo, a streamlined BFT protocol. Apollo is demonstrates to outperform existing streamlined BFT protocols such as Hotstuff, while preventing equivocation due to rollback attacks. This is all done while maintaining the reduced number of communication phases and tolerating a minority of faulty nodes.

Federated Learning is a machine learning paradigm where the computational load for training the model on the server is distributed amongst a pool of clients who only exchange model parameters with the server. Simulation environments try to accurately model all the intricacies of such a system. However, current simulators do not properly impose the concept of simulation time, leading to global model inaccuracies and difficulties of replicating reruns of the simulation, which is most prominent in the asynchronous scenarios. To this purpose, we propose a discrete-event simulator for the central server asynchronous case which timestamps all the events in the system prior to execution, reducing variability in client model updates on the server. We also introduce a log-structure used to keep states of the simulation, making client inspection possible based on time. We evaluate the proposed discrete-event simulator on the baseline simulator of Flower, reducing standard deviation amongst server model updates for 31.5% and improving accuracy with heterogeneous clients in the MNIST case for 3.3% on average.

Federated Learning has gained prominence in recent years, in no small part due to its ability to train Machine Learning models with data from users' devices whilst keeping this data private. Decentralized Federated Learning (DFL) is a branch of Federated Learning (FL) that deals with clients directly communicating with each other as opposed to using a central server. Client mobility describes how users' devices move in the real world, and its effects on the learning performance of Hierarchical Federated Learning (HFL) systems have been found to be significant. However, the effects of client mobility on DFL systems have not been explored. In this work, we fill this research gap. First, we develop a model that can describe client mobility in a DFL system. Then, using synthetic datasets, we show that client mobility has a positive impact on learning performance, which we quantify. Moreover, we show that there is a disparity in learning performance between high-mobility and low-mobility clients when using a baseline model aggregation algorithm. To address this disparity, we propose a new mobility-aware model aggregation algorithm. Our experimental results on synthetic datasets show that our solution reduces the disparity in learning performance between high- and low-mobility clients in the scenarios where this disparity is greatest, with no appreciable downsides in global learning performance.

Federated learning (FL) is a machine learning paradigm where private datasets are distributed among decentralized client devices and model updates are communicated and aggregated to train a shared global model. While providing privacy and scalability benefits, FL systems also face challenges such as client and data heterogeneity, where the training resources and different datasets are non-Independent and Identically Distributed (non-IID). When testing novel FL algorithms or configurations, simulators are used to create controlled environments without needing costly deployments. However, it is important to understand how representative FL simulators are of real-world deployments, and what steps can be taken to bridge the gap between theoretical results and practical implementations. In this paper, we investigate the effects of incorporating traces from a pseudo-real heterogeneous FL deployment in simulated environments. We compare four non-IID attributes, including batch sizes, local epochs, data volume, and data labels, to determine the most influential factors for reproducing the deployment results in simulations. We show that there is an inherent difference between deployments and simulations, despite incorporating identical non-IID conditions. Furthermore, we show that including non-IID data labels in simulations has the most significant impact on recreating the deployment outcome. We also demonstrate that incorporating the other mentioned factors has negligible impact, resulting in similar training performance compared to fully IID simulations. Our results are derived from a 20-client single-server synchronous FL configuration, and additional research is necessary to confirm our findings for larger-scale systems.

Blockchain-based payment systems typically assume a synchronous communication network and a limited workload to confirm transactions within a bounded timeframe. These assumptions make such systems less effective in scenarios where reliable network access is not guaranteed.
Offline payment systems are designed to allow users to register offline transactions and ensure their execution once network access is restored. However, these methods often rely on specialized hardware to remain secure and tamper-proof, preventing double-spending and invalid transactions.

In this work, we introduce a novel offline payment system that does not rely on trusted hardware. Our system enables users to determine transaction fees and accept offline payments based on the trustworthiness of their peers. Users can decide whether to accept offline payments from each other based on the information our system provides.

Our system employs a smart contract to manage transactions on a blockchain for its online component. We implemented the online part of our system as an Ethereum Solidity smart contract and deployed it on the Sepolia testnet. Our performance evaluation demonstrates our system’s practicality, handling 68 Transactions Per Second (TPS) with a confirmation latency of 12 seconds on the Sepolia testnet. The system’s scalability can improve with advanced layer-1 or layer-2 blockchain solutions, enhancing throughput, decreasing latency, and reducing fees.

Streamlined Byzantine Fault Tolerant (BFT) protocols, such as HotStuff [PODC'19], and weighted voting represent two possible strategies to improve consensus in the distributed systems world. Several studies have been conducted on both techniques, but the research on combining the two is scarce. To cover this knowledge gap, we introduce a weighted voting approach on Hotstuff, along with two optimisations targeting weight assignment distribution and leader rotation in the underlying state replication protocol. Moreover, the weighted protocols developed rely on studies proving the effectiveness of a specific voting power assignment based on discrete values. We generalise this approach by presenting a novel continuous weighting scheme applied to the Hotstuff protocol to highlight the effectiveness of this technique in faulty scenarios. We prove the significant latency reduction impact of weighted voting on streamlined protocols and advocate for further research.

This paper explores the integration of weighted vot-ing mechanisms into DAG-based consensus proto-cols, such as Tusk [EuroSys’22], which promise high throughput and low latency. Weighted voting,
pioneered by protocols like WHEAT [SRDS’15] and AWARE [TDSC’20], aims to optimize perfor-mance metrics such as latency and throughput by assigning weights to nodes based on their latency with other nodes. We employ real-world latency data from CloudPing to evaluate the impact of weighted voting on Tusk, comparing them against its unweighted counterpart. Our results demon-strate significant performance improvements, with the weighted voting mechanism achieving up to 37% lower consensus latency compared to un-weighted approaches.

This paper examines the impact of faulty nodes on Practical Byzantine Fault Tolerance (PBFT) algorithms, focusing on the AWARE optimization. While AWARE improves average-case latency by assigning larger voting weights to well-connected nodes, it is vulnerable to exploitation by Byzantine nodes. We propose modifications to AWARE to enhance resilience of dynamic link latency estimation. Experiments show that comparing predicted and actual consensus latencies helps detect and mitigate performance issues caused by malicious nodes. Integrating latency information with BFT-SMaRt’s leader change algorithm offers a more robust solution. We also analyse shortcomings of AWARE dynamic leader selection system.

This research addresses the challenge of managing latency in distributed computer systems. Maintaining correct delays in data transmission across various network conditions is crucial for system efficiency and security. We focus on improving the Adaptive Wide-Area Replication (AWARE) algorithm, a method used to coordinate data across different locations in a way that minimizes delays. To enhance AWARE, we incorporate two concepts: Vivaldi network coordinates and New- tonian invariants. Vivaldi network coordinates help the system better understand and calculate the physical layout of the network by embedding network members in Euclidean space, where the distance in this space represents latency be- tween them. Newton invariants are rules based on physics that help the system detect and adjust for any unusual changes in network latency that might be caused by technical issues or security threats. We evaluated the original and enhanced AWARE algorithms by simulating typical network operations and various attack scenarios designed to slow down the process of reaching consensus. Our findings show that the enhanced AWARE algorithm provides more accurate and robust management of network latency, especially under attack conditions, leading to a more reliable and secure distributed system. This study confirms that integrating correction techniques into latency management processes significantly improves the resilience and accuracy of distributed systems.

Since its emergence in 2008, blockchain technology has significantly expanded its scope, impacting various industries beyond its initial cryptocurrency applications. Its potential to enhance established practices is increasingly recognized, yet its application in the Architecture, Engineering, and Construction (AEC) industry has been relatively little researched to date, let alone been applied in practice. This research aims to develop a system facilitating the initiation and management of architectural projects on the blockchain, enabling multi-disciplinary collaboration and participation, by integrating blockchain technology into the AEC ecosystem. The proposed system is designed to allow architects, engineers, and designers, irrespective of their preferred design software, to contribute, manage and record their designs. By uploading these designs to the InterPlanetary File System (IPFS) and recording the IPFS hashes on the Ethereum blockchain, the system ensures immutable data provenance, transparent ownership representation, and heightened project transparency. Furthermore, this framework automates various aspects of project management, including contractual obligations, payments, and compliance verification through smart contracts. At the core of this research is a multi-step approach that begins with a literature review to establish the current state of blockchain applications within the AEC sector. The review focuses particularly on the potential of smart contracts, NFTs, and IPFS for improving the management of construction projects. A conceptual framework is then developed, drawing from the literature to designing a prototype system that integrates these technologies. The prototyping phase involves creating a blockchain-based system where architectural project management can be conducted securely and efficiently. Key to this phase is the crafting of smart contracts to automate project workflows and the use of NFTs for clear delineation of design ownership and achievements. The system also incorporates a method for offchain storage of design files through IPFS, connected to the blockchain, ensuring data integrity and easy retrieval. Systematic testing under certain scenario assesses performance and informs iterative optimization to refine the system's functionality. A performance evaluation strategy is employed to assess the prototype, ensuring its suitability for real-world applications in the AEC sector. This research develops a blockchain-based system in attempt to restructure project management in the AEC sector. Through iterative development, testing, and optimization, the objective of this study is to contribute to enhanced efficiency, transparency, and collaboration within the industry, exploring the potential benefits of integrating blockchain technology in a structured and measured manner.

Searchable symmetric encryption (SSE) is an encryption scheme that allows a single user to perform searches over an encrypted dataset. The advent of dynamic SSE has further enhanced this scheme by enabling updates to the encrypted dataset, such as insertions and deletions. In dynamic SSE, attackers have employed file injection attacks, initially proposed by Cash et al. (CCS 2015), to obtain sensitive information. These attacks have shown impressive performance with 100% accuracy and no prior knowledge requirement. However, they fail to recover queries with underlying keywords not present in the injected files. To address these limitations, our research introduces a novel attack strategy that incorporates the idea of inference attacks relying on uniqueness in leakage patterns. The goal is to achieve an amplified effect in query recovery. Additionally, we propose a keyword classification based on their access patterns, which helps identify the current limitation of query recovery in reference attacks. With our proposed attack, we demonstrate a minimum query recovery rate of 1.3 queries per injected keyword with a 10% data leakage of real-life datasets. Furthermore, our findings initiate further research to overcome challenges associated with non-distinctive keywords faced by inference attacks.

Effective large-scale process optimization in manufacturing industries requires close cooperation between different parties of human experts who encode their knowledge of related domains as Bayesian network models. For example, parties in the steel industry must collaboratively use their Bayesian networks on process parameters at the maker, steel properties, and application demands at the client to identify process optimizations effectively. However, business confidentiality across domains hinders collaboration, demanding alternatives to centralized inference. We propose CCBNet, the first Confidentiality-preserving Collaborative Bayesian Network inference framework. CCBNet leverages secret sharing to securely perform analysis on the combined knowledge of party models by joining two novel subprotocols: CABN, which augments probability distributions for features across modeling parties into secret shares of their normalized combination; and (ii) SAVE, which aggregate party inference result shares through distributed variable elimination. We extensively evaluate CCBNet on nine public Bayesian networks. Our results show CCBNet achieves similar predictive quality to centralized methods while preserving model confidentiality. We finally demonstrate that CCBNet scales to challenging manufacturing use cases, where involving many (16-128) parties in large networks (223-1003 features), on average, enables 45% less computation while communicating 251k values/request.

Byzantine consensus protocols are designed to build resilient systems to achieve consensus under Byzantine settings, maintaining safety guarantees under any network synchrony model and providing liveness in partially or fully synchronous networks.
However, several Byzantine consensus protocols have been shown to violate liveness properties under certain scenarios. Existing testing methods for checking the liveness of consensus protocols check for time-bounded liveness violations, which generate a large number of false positives.

In this thesis, for the first time, we check the liveness of Byzantine consensus protocols by the temperature and lasso detection methods and precisely detail ad-hoc system state abstractions that should be used to test these algorithms.
We provide a theoretical analysis of the recently-published safety and liveness attacks and whether the existing test method is able to detect them.
To investigate the issues, we focus on the streamlined blockchain consensus, particularly the HotStuff protocol family, which has been recently developed for blockchain consensus. Among these protocols, the HotStuff protocol itself is both safe and live under the partial synchrony assumption, whereas 2-Phase Hotstuff and Sync HotStuff protocols can violate liveness in subtle fault scenarios.
We implemented our liveness checking methods on top of the Twins automated unit test generator to test the HotStuff protocol family and explored the scenarios with message delay.
Our results indicate that our methods successfully detect all known liveness violations and produce fewer false positives than the bounded liveness checks.

Vertical federated learning’s (VFL) immense potential for time series forecasting in industrial applications such as predictive maintenance and machine control remains untapped. Critical challenges to be addressed in the manufacturing industry include small and noisy datasets, model explainability, and stringent privacy requirements for training and inference of forecasting. Additionally, to increase industry adaptability, such forecasting models must scale well with the parties/clients while ensuring strong convergence and low tuning complexity. To this end, we propose and design “Secret-shared Time Series Forecasting with VFL” (STV), a novel framework with the following features: i) a privacy-preserving VFL algorithm for time series forecasters such as SARIMAX and autoregressive trees, ii) secret sharing with multi-party computation protocols for aggregating intermediate training data and for privacy-preserving serverless inference, iii) extension of secure two-party matrix operations for direct parameter optimization to multiple parties, giving strong convergence with minimal hyperparameter tuning complexity. We conduct evaluations on six diverse datasets from both public and industry-specific contexts. Our results demonstrate that STV’s forecasting accuracy is comparable to those of centralized approaches and that direct optimization can outperform centralized methods by 23.81% on forecasting accuracy, including state-of-the-art diffusion models and long-short-term memory. We also conduct scalability analysis to offer the opportunity for flexible decision-making by examining the communication costs of direct and iterative optimization, allowing navigating between these two approaches.

The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Probabilistic Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training, compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score.