The paper proves that personalized federated temporal-difference learning with a shared linear representation converges at rate O(1/(N^{2/3} T^{2/3})), yielding linear speedup in the number of agents under Markovian noise.
, Edo 2: Get the initial state of the environment; 3: for t = 0, 1, ..., T− 1 do 4: for i = 1,
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On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
The paper proves that personalized federated temporal-difference learning with a shared linear representation converges at rate O(1/(N^{2/3} T^{2/3})), yielding linear speedup in the number of agents under Markovian noise.