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Federated Learning 联邦学习概述及最新工作_feddefender: client-side attack-tolerant federated

feddefender: client-side attack-tolerant federated learning

声明:以后会逐渐转移到某乎啦,有兴趣的伙伴可以关注:霁月

 

一、目前主流的联邦学习应用场景

 

Cross-silo FL

Cross-device FL

Setting

Training a model on siloed data. Clients are from different organizations (e.g. medical or financial) or geo-distributed datacenters.

The clients are a very large number of mobile or IoT (物联网) devices

医疗方面,可能有未来的家庭医生hhh

Data

distribution

Data is generated locally and remains decentralized.

Distribution

scale

Typically 2 - 100 clients.

Massively parallel, up to 10^10 clients.

Addressability

Each client has an identity or name that allows the system to access it specifically.

Clients cannot be indexed directly (i.e.,

no use of client identifiers).

不知道哪些数据已经收集,有重复手机训练的可能性

Data partition

axis

Partition is fixed. Could be horizonta

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