2020

• Federated Learning in a Medical Context: A Systematic Literature Review.Pfitzner, Bjarne; Steckhan, Nico; Arnrich, Bert in ACM Transactions on Internet Technology (TOIT) Special Issue on Security and Privacy of Medical Data for Smart Healthcare (2020).
  [ Abstract ] [ BibTeX ]
   
  Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. On the other hand, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
  @article{pfitzner2020federated, abstract = {Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. On the other hand, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.}, author = {Pfitzner, Bjarne and Steckhan, Nico and Arnrich, Bert}, journal = {ACM Transactions on Internet Technology (TOIT) Special Issue on Security and Privacy of Medical Data for Smart Healthcare}, keywords = {sys:relevantfor:connectedhealth arnrich bert bjarne connected health pfitzner}, title = {Federated Learning in a Medical Context: A Systematic Literature Review}, year = 2020 }
• Tangle Ledger for Decentralized Learning.Schmid, R.; Pfitzner, B.; Beilharz, J.; Arnrich, B.; Polze, A. (2020). 852-859.
  [ Abstract ] [ BibTeX ] [ DOI ]
   
  Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing nodes.Previous approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack surface.We propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.
  @inproceedings{schmid2020tangle, abstract = {Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing nodes.Previous approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack surface.We propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.}, author = {Schmid, R. and Pfitzner, B. and Beilharz, J. and Arnrich, B. and Polze, A.}, booktitle = {2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}, keywords = {sys:relevantfor:connectedhealth arnrich bert bjarne connected health pfitzner}, month = {may}, pages = {852-859}, title = {Tangle Ledger for Decentralized Learning}, year = 2020 }

2019

• Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions.Morassi Sasso, Ariane; Datta, Suparno; Pfitzner, Bjarne; Zhou, Lin; Steckhan, Nico; Boettinger, Erwin; Arnrich, Bert (2019).
  [ BibTeX ] [ URL ] [ DOI ]
   
  @inproceedings{MorassiSasso2019, address = {Trento}, author = {Morassi Sasso, Ariane and Datta, Suparno and Pfitzner, Bjarne and Zhou, Lin and Steckhan, Nico and Boettinger, Erwin and Arnrich, Bert}, booktitle = {Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, keywords = {sys:relevantfor:connectedhealth arnrich bert bjarne lin nico pfitzner steckhan zhou}, publisher = {EAI}, title = {Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions}, year = 2019 }
• Poisoning Attacks with Generative Adversarial Nets.Muñoz-González, Luis; Pfitzner, Bjarne; Russo, Matteo; Carnerero-Cano, Javier; Lupu, Emil C in arXiv:1906.07773 (2019).
  [ BibTeX ]
   
  @article{munozgonzalez2019poisoning, author = {Muñoz-González, Luis and Pfitzner, Bjarne and Russo, Matteo and Carnerero-Cano, Javier and Lupu, Emil C}, journal = {arXiv:1906.07773}, keywords = {sys:relevantfor:connectedhealth adversarial bjarne generative nets pfitzner}, title = {Poisoning Attacks with Generative Adversarial Nets}, year = 2019 }