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1 .Computational Challenges & Hardware Acceleration of
Cross-silo Federated Learning
Junxue ZHANG
2 .Outline
1. Background — Federated Learning & FATE
2. Computational Challenges of Federated Learning
3. Hardware Acceleration — Challenges &
Opportunities
4. Hardware Acceleration Results
3 .Background
Data Silos & Islands of Data
Company A Company B
Company D Company C
4 .Background
Federated Learning
Hospital 1 Hospital 2 Hospital 3
database database database
NN layers specialized to each
hospital and trained locally
… … …
Global NN layer trained by
All data sources in a federated manner
…
Analyzer
Cross-device Federated Learning Cross-silo Federated Learning
5 .Background
Cross-silo Federated Learning
Horizontal Federated Learning Vertical Federated Learning
6 .Background
FATE — An Industrial Grade Federated Learning
Framework
7 .Computational Challenges
Understand the Performance Challenges of Federated
Learning
Computation Overhead Networking Overhead
8 .Computational Challenges
A Fine-grained Analysis over Computational Overhead
ID Crytographic Operations ID Crytographic Operations
O1 Paillier Encryption w/ Obfuscation O6 Ciphertext & Cleartext Matrix Multiplication
O2 Paillier Encryption w/o Obfuscation O7 Ciphertext Matrix Summation
O3 Paillier Decryption O8 RSA Encryption/Decryption
O4 Ciphertext Matrix Addition O9 RSA Blind
Ciphertext & Cleartext Matrix Element-wise Multi- O1
O5 RSA Unblind
plication. O
9 .Computational Challenges
Conclusions
• Cryptographic operations do cause much performance penalty for cross-
silo federated learning applications
• Different applications may use different cryptographic operations
• Even within one application, different cryptographic operations are used at
different time
10 .Hardware Acceleration
Idea of Hardware Offloading
CPU … Cryptographic Operation 1 … Cryptographic Operation 2
Total Computation Time
CPU … …
More
CO1 CO2
Advanced Hardware
Total Computation Time
11 .Hardware Acceleration
Brief Introduction to FPGA
12 .Hardware Acceleration
One Advantage of FPGA — Low Latency
• Fixed structure • Fully-customized structure
• High-performance cache can only • 5X on-chip cache, which is distributed evenly
on the chip
serve part of the ASIC
• Large latency
• Low latency, high throughput, high
pipelining
13 .Hardware Acceleration
Straw-man Approach
O1: Paillier Encryption w/ Obfuscation
O2: Paillier Encryption w/o Obfuscation
O3: Paillier Decryption
…
O10: RSA Unblind
Problem 1: FPGA has limited programmable resources to directly bring 10 operations to a good acceleration
Problem 2: Wasted resource because not all operations are used at the same time
14 .Hardware Acceleration
Microscope these Cryptographic Operations
The core of these cryptographic operations is 2
basic operator: modular multiplication &
exponentiation
The performance of these operations largely reply
on the 2 basic operators
15 .Hardware Acceleration
Composable & Dynamic Cryptographic Operation Offloading
16 .Hardware Acceleration
Fine-grained Pipelining
Inter-engine Pipelining Intra-engine Pipelining
17 .Evaluation Results
Performance of Cryptographic Operations
Cryptographic Operations
18 .Evaluation Results
Performance of End-to-end Applications
Vertical Logistic Regression Vertical Linear Regression Horizontal Linear Regression
19 .Product
FPGA Acceleration Card
20 .Product
Product Roadmap
21 .FATE开源社区：全球首个隐私计算、联邦学习开源社区
FATE开源社区是面向隐私计算、联邦学习开源生态中的开发者、贡献者、用户及生态伙伴建立的学习与交流平台，
是全球首个隐私计算、联邦学习开源社区，拥有全球首个工业级安全联邦学习框架。社区以“开源开放，共力创新”
为愿景，不断汇集更多创新力量，现有3000+位来自近千家企业及科研机构的开发者参与社区生态共建。
杨强 唐杰
技术指导委员会主席 技术指导委员会名誉主席
⚫ 香港科技大学计算机与工程系讲席教授 ⚫ 清华大学计算机系教授、副系主任
⚫ 加拿大工程院和加拿大皇家科学院两院院士 ⚫ 清华-工程院知识智能联合实验室主任
⚫ AAAI/ACM/CAAI/IEEE/IAPR/AAAS Fellow ⚫ 国家杰青、王选杰出青年学者，IEEE Fellow
⚫ 香港人工智能与机器人学会理事长 ⚫ 香港人工智能与机器人学会理事长
22 .FATE开源社区：全球首个隐私计算、联邦学习开源社区
开 发 者 ： 用户：8000+ PR ： Github Star ：
3000+ 20+ 4.1K
• 汇集3000余位开发者参与社区共建 • 累计实名用户3500+ • 累计发布20次版本更新 • Github 累计获得4.1K Star
• 覆盖1576家企业、高校 • 社群用户超8000+ • 持续创新功能开发，充分支撑
联邦学习产业应用
23 .FATE开源社区：全球首个隐私计算、联邦学习开源社区
我们希望与各位一起，
共同打造更安全、易用的FATE开源框架！
共同构筑繁荣开放的联邦学习技术生态！
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官网：https://cn.fedai.org/
GitHub：https://github.com/FederatedAI
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