activity
20222025
most citedCollaborative Distributed Machine Learning

11 citations · 25 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2025★ 2 cited

From Concept to Measurement: A Survey of How the Blockchain Trilemma Is Analyzed

Mansur Masama Aliyu, Niclas Kannengießer, Ali Sunyaev

The blockchain trilemma highlights the difficulty of simultaneously achieving a high degree of decentralization (DoD), scalability, and security in blockchain systems. While numero…

q-fin.TR2025★ 1 cited

Automated Market Makers: A Stochastic Optimization Approach for Profitable Liquidity Concentration

Simon Caspar Zeller, Paul-Niklas Ken Kandora, Daniel Kirste +3

Concentrated liquidity automated market makers (AMMs), such as Uniswap v3, enable liquidity providers (LPs) to earn liquidity rewards by depositing tokens into liquidity pools. How…

q-fin.TR2025★ 4 cited

Automated Market Makers: Toward More Profitable Liquidity Provisioning Strategies

Thanos Drossos, Daniel Kirste, Niclas Kannengießer +1

To trade tokens in cryptoeconomic systems, automated market makers (AMMs) typically rely on liquidity providers (LPs) that deposit tokens in exchange for rewards. To profit from su…

cs.MA2023★ 11 cited

Collaborative Distributed Machine Learning

David Jin, Niclas Kannengießer, Sascha Rank +1

Various collaborative distributed machine learning (CDML) systems, including federated learning systems and swarm learning systems, with diferent key traits were developed to lever…

q-fin.TR2023★ 6 cited

Automated Market Makers in Cryptoeconomic Systems: A Taxonomy and Archetypes

Daniel Kirste, Niclas Kannengießer, Ricky Lamberty +1

Designing automated market makers (AMMs) is crucial for decentralized token exchanges in cryptoeconomic systems. At the intersection of software engineering and economics, AMM desi…

cs.LG2022★ 1 cited

Practitioner Motives to Use Different Hyperparameter Optimization Methods

Niclas Kannengießer, Niklas Hasebrook, Felix Morsbach +5

Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter…