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Authors: Mohak Chadha, Alexander Jensen, Jianfeng Gu, Osama Abboud (Huawei MRC), and Michael Gerndt Abstract: Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data…

The paper from David Hildenbrand, Martin Schulz and Nadav Amit won a distinguished artifact award. It is freely accessible on the ACM Digital Library for one month. Link. https://asplos-conference.org/

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At the Chair of Computer Architecture and Parallel Systems, Jianfeng Gu, Mohak Chadha, Anshul Jindal, and Michael Gerndt are working on the…

Every year, the Center of Doctoral Studies in Informatics and its Applications (CeDoSIA) honors the best supervisors of doctoral students of the…

Mohak Chadha’s proposal on “FedLessExit: Leveraging Early Exiting for Client Model Personalization in Serverless Federated Learning” has been accepted…

TUM Campus Run 2022

The paper by Roman Karlstetter, Robert WIdhopf-Fenk and Martin Schulz, a joint work of IfTA GmbH and CAPS, proposes an approach for retrieving sensor…

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Paper titled “Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments" got accepted at IEEE/ACM CCGrid’24.

Paper titled “Copy-on-Pin: The Missing Piece for Correct Copy-on-Write" won a Distinguished Artifact Award at ASPLOS'23

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Google Cloud Research Credits worth $5000 awarded to Jianfeng Gu for his proposal on “FaaSShare: Fine-grained GPU Resource Sharing for FaaS”

Prof. Martin Schulz receives the CeDoSIA Supervisory Award 2022

Mohak Chadha selected as a participant of the Software Campus Program with a funding of 100.000 Euros from BMBF

We did it!

Paper "Querying Distributed Sensor Streams in the Edge-to-Cloud Continuum" accepted at IEEE EDGE 2022