Cybertwin-based method for constructing two-layer federated learning framework for internet of vehicles

A cybertwin-based method for constructing a two-layer federated learning framework for internet of vehicles, comprising deploying an edge cloud server j between a vehicle device i and a central cloud server to build a two-layer federated learning system for internet of vehicles;

• the vehicle device i, the edge cloud server j and the central cloud server collaborating to complete model training;
• and the vehicle device i, the edge cloud server j and the central cloud server being communicated via a wireless link;
• and
• specifically comprising the following steps:
• step 1:
• constructing a two-layer federated learning system for the internet of vehicles, dividing a cybertwin network framework of a federated learning model for the internet of vehicles into three layers:
• the central cloud server, the edge cloud server j and the vehicle device i, with the edge cloud server j attached to a roadside unit preset in the vehicle device i, wherein
• the central cloud server signs with its preset private key SG, encrypts a global model ωt at a time t with a preset public key Pj corresponding to the edge cloud server j, and sends same to the edge cloud server j;
• and
• the edge cloud server j decrypts an encrypted global model ωt at the time t with its preset private key Sj to obtain a decrypted global model ωt, ensuring the communication security between the edge cloud server j and the central cloud server;
• and each edge cloud server j collects a list
• N
• j
• t
• =
• {
• 1
• ,
• 2
• ,
• 3
• ,
• …
• ,
• i
• }
• of the vehicle device i and sends the global model ωt to the vehicle device i;
• step 2:
• the vehicle device i training a local model
• ω
• i
• t
• using a local dataset and sending same to the edge cloud server j, the edge cloud server j acquiring a historical behavior of the vehicle device i by calculating a contribution degree score of the local model
• ω
• i
• t
• and by a cybertwin node, and discarding the local model
• ω
• i
• t
• that does not meet the requirements of a preset model, to obtain an edge cloud model
• ω
• i
• t
• +
• 1
• at a time t+1, the local model
• ω
• i
• t
• that does not meet the requirements of the preset model being:
• the local model
• ω
• i
• t
• not within a preset historical behavior, or, the local model
• ω
• i
• t
• not within a preset contribution degree score, or, the local model
• ω
• i
• t
• not within a preset quality, wherein the vehicle device i signs with the private key Si, encrypts the local model
• ω
• i
• t
• +
• 1
• at the time t+1 with a public key PG corresponding to the edge cloud server j, and sends same to the roadside unit;
• the edge cloud server j obtains a local model set
• {
• ω
• 1
• t
• +
• 1
• ,
• ω
• 2
• t
• +
• 1
• ,
• ω
• 3
• t
• +
• 1
• ,
• …
• ⁢
• ω
• i
• i
• +
• 1
• }
• submitted by the vehicle device i within a range of the roadside unit, and aggregates the local model set
• {
• ω
• 1
• t
• +
• 1
• ,
• ω
• 2
• t
• +
• 1
• ,
• ω
• 3
• t
• +
• 1
• ,
• …
• ⁢
• ω
• i
• i
• +
• 1
• }
• to obtain a model
• ω
• j
• t
• +
• 1
• of the edge cloud server j at the time t+1;
• and
• the edge cloud server j signs with its private key Sj, encrypts the model
• ω
• j
• t
• +
• 1
• of the edge cloud server j at the time t+1 with the public key PG of the central cloud server, and sends same to the central cloud server;
• step 3:
• the central cloud server receiving an edge cloud model
• {
• ω
• 1
• t
• +
• 1
• ,
• ω
• 2
• t
• +
• 1
• ,
• ω
• 3
• t
• +
• 1
• ,
• …
• ,
• ω
• i
• i
• +
• 1
• }
• and aggregating same to obtain a global model ωt+1 at the time t+1;
• and
• step 4:
• repeating steps 1-3 until the end of a global iteration, to obtain a final global model ωT.