DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation

Author:

Cheng Pau-Chen1ORCID,Eykholt Kevin1ORCID,Gu Zhongshu1ORCID,Jamjoom Hani1ORCID,Jayaram K. R.1ORCID,Valdez Enriquillo1ORCID,Verma Ashish2ORCID

Affiliation:

1. IBM Research, New York, USA

2. Amazon Inc., New York, USA

Publisher

ACM

Reference88 articles.

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2. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In International conference on artificial intelligence and statistics. PMLR, 2938--2948.

3. Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi. 2018. Personalized and private peer-to-peer machine learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 473--481.

4. Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019. Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning. PMLR, 634--643.

5. Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018).

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