arXiv subjects

Richard O. Sinnott

Publications and source records attributed to Richard O. Sinnott.

10 recordsLinked to original sources

arXiv2026

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening

Byzantine-robust decentralized federated learning (DFL) protects peer-to-peer training from malicious clients. The dominant defenses rely on similarity-based filtering, in which each client exchanges full model vectors with every neighbor before any filtering decision; this communication grows with the model dimension and scales poorly as models grow. We propose SketchGuard, which decouples screening from aggregation: clients screen neighbors in a compact Count Sketch domain and fetch full models only from those that pass the screen. We show this idea is insecure when implemented naively. Because the sketch is a fixed, publicly known linear map, an adaptive adversary can hide an arbitrarily large perturbation in its null space, so the poisoned model passes both the sketch-domain filter and the re-sketch verification. We prove this vulnerability and close it with commit-then-sketch, a one-message protocol that draws the sketch seed only after models are committed, restoring the oblivious setting in which Count Sketch provably preserves screening decisions. We then establish convergence in strongly convex and non-convex settings, with explicit dependence on network connectivity and data heterogeneity. Empirically, secured SketchGuard matches state-of-the-art full-precision robustness, up to a small threshold inflation, across six attacks including the adaptive null-space attack, a range of network topologies and heterogeneity settings, and a decentralized fine-tuning task on an 11-million-parameter language model, while reducing per-neighbor screening communication to a size independent of the model dimension.

cs.LG↗

arXiv2026

Topology-Aware Differential Privacy in Hierarchical Federated Learning

Hierarchical federated learning places regional aggregators between clients and the cloud, so a participant's update is observed only alongside its neighbours'. The concealment this arrangement provides depends on the size of the aggregation region, and regions in operational deployments vary widely. Prevailing practice applies a single noise multiplier to every participant, calibrated for the most exposed region, so every other participant carries more noise than its own exposure requires. We show that this allocation problem admits an explicit solution. We first give a silo-level differential privacy guarantee for the mechanism, then bound the mutual information between a participant's local class distribution and any estimate an observer positioned above the regional tier could form of it, using an adjacency notion matched to the quantity being protected. Minimising the worst-case bound under a fixed utility budget yields a min-max optimal allocation, which we call Fulcrum. The budget it recovers has a closed form we term the exposure dispersion, a measure of how unevenly aggregation weight is concentrated within regions relative to the most exposed one. Because this quantity follows from the region structure and the aggregation weights alone, a practitioner can evaluate it before training begins, and it vanishes precisely when all regions are equally exposed. On image and text classification at $\varepsilon = 0.99$, accuracy at a matched worst-case per-client guarantee improves by up to $14.84$ and $12.16$ percentage points where the dispersion is large, and is exactly zero on a balanced control for which the theory predicts parity.

cs.CR↗

arXiv2026

Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking

Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving accurate and trustworthy evidence. Previous methods rely on semantic and social-contextual patterns learned from training data, which limits their generalization to new data distributions. Recently, Retrieval Augmented Generation (RAG) based methods have been proposed to utilize the reasoning capability of LLMs with retrieved grounding evidence documents. However, these methods largely rely on textual similarity for evidence retrieval and struggle to retrieve evidence that captures multi-hop semantic relations within rich document contents. These limitations lead to overlooking subtle factual correlations between the evidence and the claims to be fact-checked during evidence retrieval, thus causing inaccurate veracity predictions. To address these issues, we propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence. LLM-enabled retrieval is designed to assess the claims and retrieve the most relevant knowledge subgraphs, forming structured evidence for fact verification. To augment the knowledge graph evidence, we retrieve web contents for completion. The above process is implemented as an automatic Markov Decision Process (MDP): A reasoning LLM agent decides what actions to take according to the current evidence and the claims. To adapt the MDP for fact-checking, we use prompt optimization to fine-tune the agentic LLM.

cs.AI↗

arXiv2026

Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT

Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination, offering resilience against single points of failure. However, statistical heterogeneity arising from non-identically distributed local data creates a fundamental challenge: nodes must learn personalized models adapted to their local distributions while selectively collaborating with compatible peers. Existing approaches either enforce a single global model that fits no one well, or rely on heuristic peer selection mechanisms that cannot distinguish between peers with genuinely incompatible data distributions and those with valuable complementary knowledge. We present Murmura, a framework that leverages evidential deep learning to enable trust-aware model personalization in DFL. Our key insight is that epistemic uncertainty from Dirichlet-based evidential models directly indicates peer compatibility: high epistemic uncertainty when a peer's model evaluates local data reveals distributional mismatch, enabling nodes to exclude incompatible influence while maintaining personalized models through selective collaboration. Murmura introduces a trust-aware aggregation mechanism that computes peer compatibility scores through cross-evaluation on local validation samples and personalizes model aggregation based on evidential trust with adaptive thresholds. Evaluation on three wearable IoT datasets (UCI HAR, PAMAP2, PPG-DaLiA) demonstrates that Murmura reduces performance degradation from IID to non-IID conditions compared to baseline (0.9% vs. 19.3%), achieves 7.4$\times$ faster convergence, and maintains stable accuracy across hyperparameter choices. These results establish evidential uncertainty as a principled foundation for compatibility-aware personalization in decentralized heterogeneous environments.

cs.DC↗

arXiv2024

Less is More: Unseen Domain Fake News Detection via Causal Propagation Substructures

The spread of fake news on social media poses significant threats to individuals and society. Text-based and graph-based models have been employed for fake news detection by analysing news content and propagation networks, showing promising results in specific scenarios. However, these data-driven models heavily rely on pre-existing in-distribution data for training, limiting their performance when confronted with fake news from emerging or previously unseen domains, known as out-of-distribution (OOD) data. Tackling OOD fake news is a challenging yet critical task. In this paper, we introduce the Causal Subgraph-oriented Domain Adaptive Fake News Detection (CSDA) model, designed to enhance zero-shot fake news detection by extracting causal substructures from propagation graphs using in-distribution data and generalising this approach to OOD data. The model employs a graph neural network based mask generation process to identify dominant nodes and edges within the propagation graph, using these substructures for fake news detection. Additionally, the performance of CSDA is further improved through contrastive learning in few-shot scenarios, where a limited amount of OOD data is available for training. Extensive experiments on public social media datasets demonstrate that CSDA effectively handles OOD fake news detection, achieving a 7 to 16 percents accuracy improvement over other state-of-the-art models.

cs.SI↗

arXiv2024

Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate

Autonomous vehicles are suited for continuous area patrolling problems. Finding an optimal patrolling strategy can be challenging due to unknown environmental factors, such as wind or landscape; or autonomous vehicles' constraints, such as limited battery life or hardware failures. Importantly, patrolling large areas often requires multiple agents to collectively coordinate their actions. However, an optimal coordination strategy is often non-trivial to be manually defined due to the complex nature of patrolling environments. In this paper, we consider a patrolling problem with environmental factors, agent limitations, and three typical cooperation problems -- collision avoidance, congestion avoidance, and patrolling target negotiation. We propose a multi-agent reinforcement learning solution based on a reinforced inter-agent learning (RIAL) method. With this approach, agents are trained to develop their own communication protocol to cooperate during patrolling where faults can and do occur. The solution is validated through simulation experiments and is compared with several state-of-the-art patrolling solutions from different perspectives, including the overall patrol performance, the collision avoidance performance, the efficiency of battery recharging strategies, and the overall fault tolerance.

cs.RO↗

arXiv2023

Fake News Detection Through Graph-based Neural Networks: A Survey

The popularity of online social networks has enabled rapid dissemination of information. People now can share and consume information much more rapidly than ever before. However, low-quality and/or accidentally/deliberately fake information can also spread rapidly. This can lead to considerable and negative impacts on society. Identifying, labelling and debunking online misinformation as early as possible has become an increasingly urgent problem. Many methods have been proposed to detect fake news including many deep learning and graph-based approaches. In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news. In this paper, we present a systematic review of fake news detection studies based on graph-based and deep learning-based techniques. We classify existing graph-based methods into knowledge-driven methods, propagation-based methods, and heterogeneous social context-based methods, depending on how a graph structure is constructed to model news related information flows. We further discuss the challenges and open problems in graph-based fake news detection and identify future research directions.

cs.SI↗

arXiv2023

An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems

Autonomous vehicles are suited for continuous area patrolling problems. However, finding an optimal patrolling strategy can be challenging for many reasons. Firstly, patrolling environments are often complex and can include unknown environmental factors, such as wind or landscape. Secondly, autonomous vehicles can have failures or hardware constraints, such as limited battery life. Importantly, patrolling large areas often requires multiple agents that need to collectively coordinate their actions. In this work, we consider these limitations and propose an approach based on model-free, deep multi-agent reinforcement learning. In this approach, the agents are trained to patrol an environment with various unknown dynamics and factors. They can automatically recharge themselves to support continuous collective patrolling. A distributed homogeneous multi-agent architecture is proposed, where all patrolling agents execute identical policies locally based on their local observations and shared location information. This architecture provides a patrolling system that can tolerate agent failures and allow supplementary agents to be added to replace failed agents or to increase the overall patrol performance. The solution is validated through simulation experiments from multiple perspectives, including the overall patrol performance, the efficiency of battery recharging strategies, the overall fault tolerance, and the ability to cooperate with supplementary agents.

cs.AI↗

arXiv2022

Machine Learning-based Classification of Birds through Birdsong

Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly available audio files of their birdsong. We present approaches used for data processing and augmentation and compare the results of various state of the art machine learning models. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study. Applying the models to more challenging and diverse audio files comprising 152 bird species, we achieve an accuracy of 58%

cs.LG↗

arXiv2022

A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks

In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Augmentation (DA), that can artificially generate new images out of original samples. Image augmentation strategies can vary by dataset, as different data types might require different augmentations to facilitate model training. However, the design of DA policies has been largely decided by the human experts with domain knowledge, which is considered to be highly subjective and error-prone. To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques. The goal of AutoDA models is to find the optimal DA policies that can maximize the model performance gains. This survey discusses the underlying reasons of the emergence of AutoDA technology from the perspective of image classification. We identify three key components of a standard AutoDA model: a search space, a search algorithm and an evaluation function. Based on their architecture, we provide a systematic taxonomy of existing image AutoDA approaches. This paper presents the major works in AutoDA field, discussing their pros and cons, and proposing several potential directions for future improvements.

cs.CV↗