Federated Direct Preference Optimization (FedDPO) enables privacy-preserving alignment of language models across distributed agents without centralizing sensitive preference data.
However, existing FedDPO analysis assumes all clients converge to a single shared policy, which is fundamentally at odds with the heterogeneous preference distributions present in practice.
We propose Personalized FedDPO (pFedDPO), in which each client maintains a small private LoRA adapter atop a shared global base model.
In pFedDPO, the base model is synchronized via FedAvg each round, while LoRA adapters remain local and capture client-specific preference patterns.
We prove that this decomposition replaces the gradient diversity $κ^2$ in the FedDPO bound with a residual heterogeneity $κ^2_{\mathrm{eff}}$, which is no larger than $κ^2$ when adapters absorb client-specific directions, at the cost of an additive adapter-bias term that vanishes as adapters converge.
Experiments on preference datasets show that pFedDPO consistently attains a lower gradient norm than vanilla FedDPO while remaining competitive in training loss and per-client reward accuracy.