Robust Methods for Distributed Learning

This thesis develops robust and efficient aggregation methods for distributed learning and explores their vulnerabilities. Distributed learning paradigms, such as federated and decentralized learning, enable the coordination of models across a collection of agents without the need to exchange raw data. Instead, agents compute model updates locally and share the updated models with a parameter server or their peers, followed by an aggregation step traditionally based on averaging. However, averaging-based schemes are susceptible to outliers, where a single malicious agent can significantly degrade the model's performance. This vulnerability has led to the development of robust aggregation schemes based on variations of the median and trimmed mean, which ensure robustness but at the cost of reduced efficiency. To address this drawback, efficient and robust aggregation schemes for distributed learning are developed in this thesis. We propose robust and efficient diffusion, a distributed learning framework which combines statistical efficiency with robustness. Additionally, a robust distributed Expectation-Maximization algorithm based on Real Elliptically Symmetric distributions is developed, which is highly adaptive to outliers and incorporates a robust data aggregation step, based on robust and efficient diffusion, to provide robustness against malicious agents. Simulations demonstrate the effectiveness of the proposed algorithms over non-robust methods. The motivation for studying attacks on robust aggregation schemes in decentralized learning is to understand and mitigate vulnerabilities that can significantly degrade the performance of learning algorithms. By developing and analyzing attack strategies, we can identify weaknesses in existing schemes and design more resilient and secure aggregation methods. This research enhances the robustness, reliability, and trustworthiness of decentralized learning, ensuring data integrity and improving overall security against adversarial scenarios. By leveraging the sensitivity curve, a classical tool from robust statistics, we systematically derive optimal attack patterns against arbitrary robust aggregators, rendering them ineffective in many cases. The new attack pattern, termed sensitivity curve maximization, disrupts existing robust aggregation schemes by injecting optimal perturbations. This highlights the vulnerability of classical aggregation schemes to malicious agents, leading to the degeneration or breakdown of the learning process. The effectiveness of the proposed attack is demonstrated through multiple simulations. Overall, this dissertation enhances the robustness and efficiency of aggregation methods in distributed learning while identifying and mitigating vulnerabilities through the development of optimal attack strategies

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems

The full story of 1000 cores : An examination of concurrency control on real(ly) large multi-socket hardware

In our initial DaMoN paper, we set out the goal to revisit the results of "Starring into the Abyss [...] of Concurrency Control with [1000] Cores" (Yu in Proc. VLDB Endow 8: 209-220, 2014). Against their assumption, today we do not see single-socket CPUs with 1000 cores. Instead, multi-socket hardware is prevalent today and in fact offers over 1000 cores. Hence, we evaluated concurrency control (CC) schemes on a real (Intel-based) multi-socket platform. To our surprise, we made interesting findings opposing results of the original analysis that we discussed in our initial DaMoN paper. In this paper, we further broaden our analysis, detailing the effect of hardware and workload characteristics via additional real hardware platforms (IBM Power8 and 9) and the full TPC-C transaction mix. Among others, we identified clear connections between the performance of the CC schemes and hardware characteristics, especially concerning NUMA and CPU cache. Overall, we conclude that no CC scheme can efficiently make use of large multi-socket hardware in a robust manner and suggest several directions on how CC schemes and overall OLTP DBMS should evolve in future

Patterns of deadwood amount and deadwood diversity along a natural forest recovery gradient from agriculture to old-growth lowland tropical forests

Deadwood is a key component of nutrient cycling in natural tropical forests, serving as a globally important carbon storage and habitat for a high number of species. The conversion of tropical forests to agriculture modifies deadwood pools, but we know little about deadwood dynamics in forests recovering from human disturbance. Here we quantified the volume and diversity of coarse woody debris (CWD, ≥ 7 cm diameter) and the mass of fine woody debris (FWD, < 7 cm) along a chronosequence of natural forest recovery in the lowlands of the Ecuadorian Chocó region. We sampled forest plots ranging from 1–37 years of recovery post-cessation of agricultural use as either cacao plantation or cattle pasture, as well as actively managed cacao plantations and cattle pastures, and old-growth forests. In contrast to our expectation, we found no significant increase in deadwood volume with recovery time. The diversity in size, decay stage and type of CWD increased along the recovery gradient, with no effect of previous land use type. The mass of FWD increased overall across the recovery gradient, but these results were driven by a steep increase in former pastures, with no change observed in former cacao plantations. We suggest that the range of sizes and decomposition stages of deadwood found in these two major tropical agricultural systems could provide suitable resources for saproxylic organisms and an overlooked carbon storage outside old-growth forests. Our estimates of deadwood in agricultural systems and recovering forests can help improve global assessments of carbon storage and release in the tropics

Parametric study of the phase diffusion process in a gain-switched semiconductor laser for randomness assessment in quantum random number generator

The quantum phase noise in a pulsed semiconductor laser is studied thoroughly in the context of its utilization as a quantum entropy source in a quantum random number generator (QRNG) device. We performed a numerical analysis of the phase diffusion process for a semiconductor laser in the continuous-wave operation mode and gain-switched (GS) mode. The result demonstrates the amplification of randomness in the GS mode, which is gauged physically by the variance VarΔϕ. The variance value, which is mathematically related to the temporal distance between the laser pulses used in the experimental setup, also determines the stability of the setup. Furthermore, we show how the QRNG probability distribution is influenced by several experimental factors such as the quality of the interference process and the noise in the detection system

¹H, ¹³C and ¹⁵N chemical shift assignment of the stem-loop 5a from the 5′-UTR of SARS-CoV-2

The SARS-CoV-2 (SCoV-2) virus is the causative agent of the ongoing COVID-19 pandemic. It contains a positive sense single-stranded RNA genome and belongs to the genus of Betacoronaviruses. The 5′- and 3′-genomic ends of the 30 kb SCoV-2 genome are potential antiviral drug targets. Major parts of these sequences are highly conserved among Betacoronaviruses and contain cis-acting RNA elements that affect RNA translation and replication. The 31 nucleotide (nt) long highly conserved stem-loop 5a (SL5a) is located within the 5′-untranslated region (5′-UTR) important for viral replication. SL5a features a U-rich asymmetric bulge and is capped with a 5′-UUUCGU-3′ hexaloop, which is also found in stem-loop 5b (SL5b). We herein report the extensive ¹H, ¹³C and ¹⁵N resonance assignment of SL5a as basis for in-depth structural studies by solution NMR spectroscopy

Cabanel, Patrick: L’école du peuple? Histoire d’une hypocrisie sociale, 128 S., PUR, Rennes 2023.

Der französische Religionshistoriker Patrick Cabanel hat mit „L'Ecole du Peuple“ eine nur 128 Seiten starke Schrift vorgelegt, die es aber in sich hat. Der Untertitel des in der „Collection Épures“ bei PUR erschienenen Büchleins zeigt das bereits an: Cabanel möchte die „histoire d’une hypocrisie sociale“, also die Geschichte einer sozialen Heuchelei, schreiben

Study on the Impact of Data Breaches on Firm Value and Information Security Risk Disclosures

The dissertation explores the financial and managerial impacts of data breaches on healthcare companies in the U.S., a sector increasingly significant given its growing proportion of the GDP and superior performance compared to the S&P 500. The study assesses the financial repercussions of data breaches and investigates the changes in disclosure practices over time. The research is divided into three parts. The first part employs an event study methodology to evaluate the stock price impacts of data breaches, revealing that while such incidents typically result in negative market reactions, these effects have diminished over time. This suggests an evolving investor sensitivity, influenced by historical market events and changes in the regulatory environment such as the implementation of breach notification laws. The second part of the thesis expands on these findings by analyzing the textual content of company disclosures following data breaches. It examines how management's response strategies affect investor reactions. The results indicate that companies can mitigate the losses in firm value typically associated with data breaches by disclosing information security risks in more detail. The third section delves deeper into the impacts of the 2018 SEC guidance on disclosure practices. It uses cosine similarity to assess changes in the textuality of disclosures post-guidance, highlighting a trend towards an industry specific convergence of information security risk disclosures following the guidance from the SEC

Learning Generalized Nash Equilibria in a Class of Convex Games

We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel distributed payoff-based algorithm, where each agent uses information only about its cost value and the constraint value with its associated dual multiplier. We prove convergence of this algorithm to a Nash equilibrium, under the assumption that the game admits a strictly convex potential function. In the absence of coupling constraints, we prove convergence to Nash equilibria under significantly weaker assumptions, not requiring a potential function. Namely, strict monotonicity of the game mapping is sufficient for convergence. We also derive the convergence rate of the algorithm for strongly monotone game maps

Arbitrary photonic wave plate operations on chip: Realizing Hadamard, Pauli-X and rotation gates for polarisation qubits

Chip-based photonic quantum computing is an emerging technology that promises much speedup over conventional computers at small integration volumes. Particular interest is thereby given to polarisation-encoded photonic qubits and many protocols have been developed for this encoding. However, arbitrary wave plate operation on chip are not available so far, preventing from the implementation of integrated universal quantum computing algorithms. In our work we close this gap and present Hadamard, Pauli-X and rotation gates of high fidelity for photonic polarisation qubits on chip by employing a reorientation of the optical axis of birefringent waveguides. The optical axis of the birefringent waveguide is rotated due to the impact of an artificial stress field created by an additional modification close to the waveguide. By adjusting this length of the defect along the waveguide, the retardation between ordinary and extraordinary field components is precisely tunable including half-wave plate and quarter-wave plate operations. Our approach demonstrates the full range control of orientation and strength of the induced birefringence and thus allows arbitrary wave plate operations without affecting the degree of polarisation or introducing additional losses to the waveguides. The implemented gates are tested with classical and quantum light