Timezone: »

Privacy-Preserving Data Filtering in Federated Learning Using Influence Approximation
Ljubomir Rokvic · Panayiotis Danassis · Boi Faltings
Event URL: https://openreview.net/forum?id=EFji8CcNlIh »
Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present a novel technique for filtering, and scoring data based on a practical influence approximation (`lazy' influence) that can be implemented in a privacy-preserving manner. Each agent uses his own data to evaluate the influence of another agent's batch, and reports to the center an obfuscated score using differential privacy. Our technique allows for highly effective filtering of corrupted data in a variety of applications. Importantly, the accuracy does not degrade significantly, even under really strong privacy guarantees ($\varepsilon \leq 1$), especially under realistic percentages of mislabeled data.

Author Information

Ljubomir Rokvic (EPFL)
Panayiotis Danassis (Harvard University)
Boi Faltings (EPFL)

More from the Same Authors

  • 2019 : Lunch break and poster »
    Felix Sattler · Khaoula El Mekkaoui · Neta Shoham · Cheng Hong · Florian Hartmann · Boyue Li · Daliang Li · Sebastian Caldas Rivera · Jianyu Wang · Kartikeya Bhardwaj · Tribhuvanesh Orekondy · YAN KANG · Dashan Gao · Mingshu Cong · Xin Yao · Songtao Lu · JIAHUAN LUO · Shicong Cen · Peter Kairouz · Yihan Jiang · Tzu Ming Hsu · Aleksei Triastcyn · Yang Liu · Ahmed Khaled Ragab Bayoumi · Zhicong Liang · Boi Faltings · Seungwhan Moon · Suyi Li · Tao Fan · Tianchi Huang · Chunyan Miao · Hang Qi · Matthew Brown · Lucas Glass · Junpu Wang · Wei Chen · Radu Marculescu · tomer avidor · Xueyang Wu · Mingyi Hong · Ce Ju · John Rush · Ruixiao Zhang · Youchi ZHOU · Fran├žoise Beaufays · Yingxuan Zhu · Lei Xia