Patent ID: 11956726
Assignee: SHANDONG UNIVERSITY
Field: Digital communication (Electrical engineering)
Classification: CPC H  Y | IPC H

Claim 2:
3. The dynamic power control method for resisting multi-user parameter biased aggregation in federated learning according to claim 1, wherein in step (2), a defined learning objective is to minimize an empirical loss function, and the objective function is shown in Formula (I):, min
        
         
          w
          ∈
         
         
          ||
          q
         
        
       
       
        F
        ⁡
        (
        w
        )
       
      
      =
      
       
        min
        
         w
         
          ∈
          q
         
        
       
       
        1
        
         
          ❘
          "\[LeftBracketingBar]"
         
         D
         
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          "\[RightBracketingBar]"
         
        
       
       ⁢
       
        
         ∑
         
          ∀
          
           
            (
            
             x
             ,
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            )
           
           ∈
           D
          
         
        
        
         f
         ⁡
         (
         
          w
          ,
          x
          ,
          y
         
         )
        
       
      
     
     ,
    
   
   
    
     (
     I
     )
    
   
  
 

in Formula (I), F(w) represents a global average training loss, and the objective of the learning process is to minimize a global loss function; w is the federated learning system model parameter vector, w=[wl, . . . , wq]T ∈□1q is the number of vector parameters, wq is the single element of the federated learning system model parameter vector; f (w, x, y) is a sample loss function; and a framework of federated learning is configured to minimize F(w) in a distributed manner and obtain a local optimal point with full-batch gradient descent:

w(n+1)=w(n)−δg(n)  (II),

W =W
in Formula (II), δ is the learning rate, w (n) is a federated learning system model parameter vector of the nth communication, and after the kth user receives the model parameter w(n) broadcast by the central server, each user node computes a corresponding local training loss and a corresponding gradient according to local data and the corresponding labels owned by the user node, and obtains a computed gradient g(n) shown in Formula (III):, g
       
        (
        n
        )
       
      
      =
      
       
        ∇
        
         F
         ⁡
         (
         
          w
          
           (
           n
           )
          
         
         )
        
       
       =
       
        
         1
         
          
           ❘
           "\[LeftBracketingBar]"
          
          D
          
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           "\[RightBracketingBar]"
          
         
        
        ⁢
        
         
          ∑
          
           ∀
           
            
             (
             
              x
              ,
              y
             
             )
            
            ∈
            D
           
          
         
         
          f
          ⁡
          (
          
           w
           ,
           x
           ,
           y
          
          )
         
        
       
      
     
     ,
    
   
   
    
     (
     III
     )
    
   
  
 

in Formula (III), an ith element of the vector g(n) is the gradient of F(w(n) relative to wi(n);
for n communication cycles, the kth user first computes a local stochastic gradient shown in Formula (IV):, g
        ˜
       
       k
       
        (
        n
        )
       
      
      =
      
       
        ∇
        
         
          F
          k
         
         (
         
          w
          
           (
           n
           )
          
         
         )
        
       
       =
       
        
         1
         
          n
          k
         
        
        ⁢
        
         
          ∑
          
           ∀
           
            
             (
             
              
               x
               l
              
              ,
              
               y
               l
              
             
             )
            
            ∈
            
             
              D
              ~
             
             k
            
           
          
         
         
          ∇
          
           f
           ⁡
           (
           
            
             w
             
              (
              n
              )
             
            
            ,
            
             x
             l
            
            ,
            
             y
             l
            
           
           )
          
         
        
       
      
     
     ,
    
   
   
    
     (
     IV
     )
    
   
  
 

in Formula (IV), {tilde over (D)}k ∈ Dk is selected data batch from local data set r, nk=|{tilde over (D)}k| is used as the batch size for computing a local gradient estimate, Fk(w(n)) represents the local average training loss of the kth user, and f(w(n), wl, yl) is the training loss of the training data point (xl, yl) in the kth user node;
if the local gradient estimate is reliably transmitted to the edge server, the global estimate of the gradient of the loss function in Formula (I) is to be computed, as shown in Formula (V):, g
        ~
       
       
        (
        n
        )
       
      
      =
      
       
        1
        K
       
       ⁢
       
        
         ∑
         
          k
          =
          1
         
         K
        
        
         g
         k
         
          (
          n
          )
         
        
       
      
     
     ,
    
   
   
    
     (
     V
     )
    
   
  
 

the steps in (IV), (V), and (II) are then continuously iterated until the convergence condition is satisfied.