2-1 联邦学习的核心挑战和对策.pdf

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1、Federated Learning:Challenges and Solutions吴方照 微软亚洲研究院 主管研究员|2Privacy is Important for AI AI relies on data for model training and online serving Highly privacy sensitive in many scenarios Strict laws on user privacy protection3Federated Learning Collaboratively learn a shared model while keeping da

2、ta on device Decouple the ability of learning from the need of data centralizationCommunication-Efficient Learning of Deep Networks from Decentralized Data,AISTATS 20174Applications of Federated Learning Examples Gboard text prediction Siri personalization5Federated Learning:Key ChallengesHeterogene

3、ityFederatedLearningEfficiencyPrivacySecurity6Federated Learning:Our WorksHeterogeneityEfficiencyPrivacySecurity FedKD Efficient-FedRec FedX InclusiveFL FedGNN FedCTR UA-FedRec FedPrompt PrivateFL FedAttack RobustFLFederatedLearning7Federated Learning:Our WorksHeterogeneityEfficiencyPrivacySecurity

4、FedKD Efficient-FedRec FedX InclusiveFL FedGNN FedCTR UA-FedRec FedPrompt PrivateFL FedAttack RobustFLFederatedLearning AI models are bigger and bigger Communication cost between client and server is huge8FedKD:Motivation9FedKD:ModelCommunication-efficient federated learning via knowledge distillati

5、on,Nature Communications10FedKD:Experiments News recommendation11FedKD:Experiments Medical text classification12Federated Learning:Our WorksHeterogeneityEfficiencyPrivacySecurity FedKD Efficient-FedRec FedX InclusiveFL FedGNN FedCTR UA-FedRec FedPrompt PrivateFL FedAttack RobustFLFederatedLearning B

6、ig AI models are expensive to learn Clients usually have weak computing capability 13Efficient-FedRec:Motivation Sub-models may have different privacy and computing requirements Split learning14Efficient-FedRec:Motivation15Efficient-FedRec:ModelEfficient-FedRec:Efficient Federate