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Recent Submissions
Fast, Private and Fair Federated Learning
(University of Waterloo, 2025-06-16) Malekmohammadi, Saber
A remarkable progress in machine learning (ML) technologies has happened during the past decade. Federated learning (FL) is a decentralized ML setting, where some clients collaborate with each other to train a model under the orchestration of a central server. Clients, for instance, can be some mobile devices (in cross-device FL) or some organizations (in cross-silo FL). In FL, the training data of clients remain decentralized, mitigating many systemic privacy risks and costs existing in centralized ML. This thesis investigates three main sub-problems of FL, including: 1. Convergence analysis of FL algorithms 2. Providing formal data privacy guarantees to clients through differentially private FL (DPFL) 3. Performance fairness for clients in DPFL systems under structured data heterogeneity.
Over the past years, the FL community has witnessed a plethora of new algorithms. However, there is a lack of thorough comparison of these algorithms and the theory behind them. Our fragmented understanding of the theory behind the existing algorithms is the reason behind the lack of such a formal comparison. This is the main focus of the first chapter, where we show that many of the existing FL algorithms can be understood from the lens of operator splitting theory. This unification allows us to compare different algorithms easily, to refine previous convergence results and to propose new algorithmic variants. Our new theoretical findings reveal the remarkable role played by the step size in FL algorithms. Furthermore, the unification allows us to propose a simple and economic way to accelerate the convergence of different algorithms, which is indeed vital, as communication between the server and clients is often considered as an overhead in FL systems.
Despite the fact that FL provides clients with some level of informal data privacy by operating on their decentralized data, the orchestrating server or other malicious third parties can still attack the data privacy of participating clients. Consequently, FL has been augmented with differential privacy (DP) in order to provide rigorous data privacy guarantees to clients. In DPFL, which is the focus of the second chapter, depending on clients’ privacy policies, there is often heterogeneity in their privacy requirements. This heterogeneity as well as the heterogeneity in batch and/or dataset sizes of clients lead to a variation in the DP noise level across clients’ model updates. Hence, straightforward aggregation strategies, e.g., assigning clients’ aggregation weights proportional to their privacy parameters, will lead to a low utility for the system. We propose a DPFL algorithm which efficiently estimates the true noise level in clients’ model updates and uses an aggregation strategy to reduce the noise level in the aggregated model update. Our proposed method improves utility and convergence speed, while being safe to the clients that may maliciously send falsified privacy parameters to the server to attack the system’s utility.
Finally, in the third chapter, we investigate the intersection of FL, DP and performance fairness, under structured data heterogeneity across clients. This type of data heterogeneity is often addressed through clustering clients (a.k.a. clustered FL). However, the existing clustered FL methods remain sensitive and prone to errors, further exacerbated by the DP noise in the system. In the third chapter, we propose a robust algorithm for differentially private clustered FL. To this end, we propose to use large batch sizes in the first round and cluster clients based on both their model updates as well as their training loss values. Furthermore, for clustering clients’ model updates at the end of the first round, our proposed approach addresses the server’s uncertainties by employing Gaussian Mixture Models (GMM) to reduce the impact of DP and stochastic noise and avoid potential clustering errors. This idea is efficient especially in privacy-sensitive scenarios with more DP noise and leads to a high accuracy in clustering clients.
Evaluating Municipal Climate Action: An Analysis of Performance Measurement Models, Practices, and Indicators
(University of Waterloo, 2025-06-16) Feor, Leah
This thesis provides interconnected contributions to theory and practice in climate governance, specifically on municipal performance measurement practices. Key concepts, including the evaluation and control step of the strategic management process, the performance measurement process, and the indicator framework and selection process, frame this research. This thesis contains five chapters. The first and last chapters serve as the introduction and conclusion, respectively. The second, third, and fourth chapter are standalone papers. The first paper of this thesis explored the current state of social impact measurement (SIM) by examining common practices that are used to measure the post-intervention social impact of programs and projects. Using a systematic literature review, this study analyzed a decade's worth of global academic literature on SIM. Through deductive and inductive manual coding of articles in NVivo, this study identified key themes and strategies for improving measurement practices. Findings from this paper suggest strategies for improved measurement such as stakeholder engagement throughout the measurement process, utilizing existing operational data, enhancing measurement capacity, and using a combination of qualitative and quantitative data. This study contributes to the SIM field by offering an in-depth understanding of common measurement models and providing clear recommendations for practitioners to improve SIM. The second paper of this thesis used a contingency theory lens to investigate the climate-related performance measurement practices of 31 Canadian municipalities, with a focus on the influence of population size. Using a case study approach, data were gathered through interviews and document analysis. Data were analyzed through both deductive and inductive coding in NVivo 14. Results indicate that municipalities with larger population sizes prioritize more themes for measurement, employ a broader set of criteria for indicator selection, and report more frequently. Population size does not seem to influence stakeholder involvement in indicator selection or data analysis strategies. By applying contingency theory to Chapter 3, this study examined a situational approach versus the idea of a ‘one-size-fits-all’ solution for local climate-related performance measurement. The final paper explored the climate mitigation indicators currently used in practice and identified those most suitable for measuring local climate-related performance. A document analysis was conducted to identify the climate-related indicators in use by 21 Canadian municipalities, which were categorized and analyzed according to the logic model framework and GHG emissions activity sectors. An indicator evaluation matrix was employed to propose a parsimonious set of 19 new climate mitigation indicators, with the Delphi technique used to achieve consensus among experts. This study found that while a range of indicators exist across the logic model, there is an uneven distribution. The analysis also revealed the emergence of nature-based indicators for local climate mitigation performance measurement. Together, this thesis showcases and defines models and frameworks that municipalities can use to better track their climate performance, while also contributing to the broader academic discourse on measurement practices in the public sector. The findings from this thesis outline streamlined approaches to performance measurement, providing clear pathways for municipalities that are looking to more effectively track progress towards common climate goals.
Impacts of temperature variation on duckweed population growth and distribution in a changing climate
(University of Waterloo, 2025-06-16) Andrade-Pereira, Debora
Understanding the impacts of climate change on aquatic plants involves examining how temperature fluctuation patterns influence their temperature-dependent vital rates and distribution. Duckweeds, small aquatic plants with both economic significance and ecological concern, can pose challenges due to overgrowth and the spread of invasive species. The impact of climate-induced temperature changes on aquatic plants remains poorly understood, as many studies use constant conditions that do not account for natural variability in temperature. Research focused on increased average temperatures has shown general ectotherm responses tied to geographic location, such as enhanced growth in temperate regions. However, when temperature fluctuations are considered, responses differ from those under constant conditions due to nonlinear and asymmetrical thermal performance. Increased autocorrelation, with prolonged sequences of unusually high or low temperatures, can affect population growth rates, while nighttime warming alters diel temperature variation and potentially influences time-sensitive processes like photosynthesis and respiration. This thesis investigates the thermal performance and distribution of duckweed species under varying temperature regimes associated with climate change, incorporating both controlled experiments and predictive modeling.
The second chapter uses a Maxent species distribution model to predict the potential range expansion of Landoltia punctata (dotted duckweed), an invasive, herbicide-resistant species. Habitat suitability is modeled under current and future climate scenarios, using satellite-derived water temperature data and constraining model features to match the shape of thermal performance curves obtained from laboratory experiments. Results indicate high suitability for this species in Western Europe and Southern Canada, with the Great Lakes region becoming increasingly suitable in the future due to climate warming. These projections underscore the importance of climate-informed management strategies to mitigate the ecological impact of invasive species.
The third chapter investigates how diel temperature variability and climate change affect the reproduction of Lemna minor (common duckweed) during spring and summer. Experimental results highlight the importance of temperature variance as opposed to the timing of warming. While increased mean spring temperatures enhance duckweed performance, reduced temperature variance during high summer temperatures in regions such as Canada helps mitigate the negative impacts of otherwise excessively hot fluctuating conditions. These findings emphasize the varying effects of climate change on duckweed's thermal performance across different seasons.
The fourth chapter examines the effects of temperature autocorrelation on both common and dotted duckweed reproduction and survival. Experiments show that strongly autocorrelated sequences result in mortality due to heat stress when hot temperature sequences begin with elevated heat. In contrast, autocorrelation has limited impacts under cooler average conditions, likely due to slower physiological responses. These findings align with broader predictions of increased extinction risks for ectotherms under persistent and extreme temperature patterns caused by climate change.
This work is a step towards a more realistic understanding of aquatic plant responses to climate change by considering thermal performance responses, diverse temperature fluctuation patterns, and water temperatures. Our results can be used in population dynamics models to make more realistic predictions of climate change responses. The experimental and modeling findings in this thesis advance our understanding of aquatic plant responses to climate change and support the development of informed strategies to manage their ecological impacts and sustainable production in a warming world.
Kinetic Energy Spectra, Backscatter, and Subgrid Parameterization Analysis in Radiative-Convective Equilibrium
(University of Waterloo, 2025-06-16) Lai, Kwan Tsaan
This thesis explores how energy is distributed and transferred across scales in convective-permitting radiative-convective equilibrium (RCE) simulations and how these processes can be more accurately represented in numerical models through improved subgrid parameterizations. Aggregation steepens the horizontal kinetic energy spectra by enhancing the large-scale energy, which results in horizontal kinetic energy spectra in both the upper troposphere and lower stratosphere that are close to the mesoscale -5/3 spectrum. In the upper troposphere, spectral energy budget analysis indicates that this is the result of the balance between buoyancy flux and vertical energy flux, rather than a classic direct energy cascade. In the lower stratosphere, there is inverse energy transfer, which may be explained by wave-mean-flow-interaction. Subfilter energy transfer analysis is performed on an idealized RCE simulation by filtering 1-km high-resolution simulation to a horizontal scale of 4 km. The net subfilter energy transfer rate is dissipative in the upper troposphere and backscattering in the lower stratosphere, which are consistent with the direction of energy transfer in the nonlinear transfer energy flux. The stochastic backscatter TKE scheme, a stochastic backscatter-allowing subgrid turbulence scheme created by adding a zero-mean stochastic forcing to the eddy viscosity of the TKE scheme, is proposed and tested on idealized RCE simulations. The stochastic backscatter TKE scheme improves the subgrid local energy transfer when compared to a common stochastic backscatter scheme and the standard TKE scheme without backscatter. Despite the fact that backscatter is still weaker than dissipation in the lower stratosphere in the stochastic backscatter TKE simulations, the kinetic energy spectra are closer to the -5/3 spectrum when compared to the standard TKE simulation. This study advances our understanding of the interscale distribution and transfer of energy in RCE, and the introduction of the stochastic backscatter TKE scheme provides a more realistic representation of dissipation and backscatter by matching the distribution of subfilter energy transfer rate from a high-resolution simulation.
Radar Near-Field Sensing For Biomedical Applications
(University of Waterloo, 2025-06-16) Bagheri, Omid
Biomedical sensing technologies are essential for real-time health monitoring and disease management. Among their applications, blood glucose monitoring is of particular importance due to the global prevalence of diabetes that demands early detection and continuous management. Although clinically approved invasive methods exist, they are often inconvenient and unsuitable for continuous monitoring. Despite extensive research, non-invasive glucose monitoring, whether through wearables or smartwatches, remains an unsolved challenge, with no commercially or clinically validated solutions available.
Radar-based biomedical sensing offers a promising non-invasive, continuous monitoring approach with tissue penetration capabilities. However, challenges such as suboptimal antenna design, near-field limitations, air-skin impedance mismatch, and poor depth resolution persist. A key objective in improving radar sensing performance is to maximize the transmitted power density from the radar's transmit (TX) antenna into the target medium while simultaneously enhancing the reflected power received by the receive (RX) antenna. This dual enhancement significantly improves the radar’s signal-to-noise ratio (SNR). Integrating advanced lenses and metasurfaces addresses these limitations, enabling efficient, practical deployment without major redesigns.
This research introduces the development and implementation of novel radar-based methodologies tailored for biomedical sensing applications. Through innovative system design, advanced signal processing, and rigorous experimental validation, the proposed solutions address key challenges in on-body sensing. The first contribution focuses on advanced lens designs, such as dielectric rod arrays and modified gradient-index (GRIN) Luneburg lenses, aimed at enhancing radar-based external health monitoring at 10 GHz.
The second contribution advances metasurface technologies for internal biomarker monitoring, enabling compact, skin-contact wearable systems with enhanced sensitivity and spatial resolution, with a specific emphasis on non-invasive blood glucose detection. Operating in the 58–63 GHz millimeter-wave band, the proposed metasurface-enhanced radar system integrates the BGT60TR13C sensor from Infineon Technologies with a planar, phase-synthesized metasurface for near-field focusing within the skin dermis layer, achieving over 11-fold improvement in SNR by enhancing both transmitted and reflected power. Four progressively objectives are presented, each expanding upon the foundation of the previous stage: single-band, single-focus metasurface, a preliminary design serving as proof of concept; metasurface-enhanced multi-radar fusion for distributed sensing; dual-band, dual-focus metasurface for depth-selective monitoring; and a multi-band, multi-focus non-interleaved metasurface for combined spatial and depth resolution. These innovations have the potential to revolutionize non-invasive, continuous health monitoring.