Abstract: The fusion of federated learning and differential privacy can provide more comprehensive and rigorous privacy protection, thus attracting extensive interests from both academia and industry.
This enables building network applications (HTTP servers, proxies, etc.) that bypass the kernel network stack entirely, achieving lower latency and higher throughput. Benchmarks shows 2X throughput ...
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