Federated Reinforcement Learning Algorithm with Attention-Based Data Selection

This is a pseudocode for a federated reinforcement learning algorithm. The algorithm involves multiple local agents and a central server. Here are the steps:

1. Initialize local model parameters w'k for each agent k.
2. Initialize global model parameters W at the central server.
3. Initialize attention scores A'ik for each data point i in k.
4. Repeat for T iterations: a. For each local agent k: i. Observe current states s'ij for each modality j. ii. Take action a't based on policy derived from Q(s, a; w'k). iii. Observe reward r't and next states s''i,j for each modality j. iv. Compute TD error δ = r't + γ * max'a Q(s''i,j, a; w'k) - Q(s'ij, a't; w'k). v. Update Q(s'ij, a't; w'k) = Q(s'ij, a't; w'k) + α * δ. vi. Update attention scores A'ikj = A'ikj + η * |δ|. vii. Send local model parameters w'k and attention scores A'ikj to Central Server. b. For each data point i: i. If P(k=1/m * P(j) A'ikj / K) < θ, reduce influence of data point i in the global model. c. Aggregate local model parameters to update global parameters: W = Σ'k(n'k/N) * w'k. d. Send updated global model parameters W to local agents. e. For each local agent k, fine-tune local model with global model: w''k = β * W + (1 - β) * w'k. f. If |P(W't+1) - P(W't)| < ε, break the loop.
5. Return the trained global model parameters W.

Note: The variables T, θ, α, η, γ, β, and ε are hyperparameters that need to be tuned according to the specific problem and data.