Title: Label-distribution-agnostic Ensemble Learning on Federated Long-tailed Data. Abstract: Federated Learning (FL) is a distributed machine learning paradigm that enables devices to collaboratively train a shared model. However, the long-tailed distribution in nature deteriorates the performance of the global model, which is difficult to address due to data heterogeneity, e.g., local clients may exhibit diverse imbalanced class distributions. Moreover, existing re-balance strategies generally utilize label distribution as the class prior, which may conflict with the privacy requirement of FL. To this end, we propose a Label-Distribution-Agnostic Ensemble (LDAE) learning framework to integrate heterogeneous data distributions using multiple experts, which targets to optimize a balanced global objective under privacy protection. In particular, we derive a privacy-preserving proxy from the model updates of clients to guide the grouping and updating of multiple experts. Knowledge from clients can be aggregated via implicit interactions among different expert groups. We theoretically and experimentally demonstrate that (1) there is a global objective gap between global and local re-balance strategies\footnote{The local re-balance strategy means that each client utilizes re-balance methods based on the local label distribution, while the global re-balance strategy applies re-balance methods using global label distribution as the class-wise prior.} and (2) with protecting data privacy, the proxy can be used as an alternative to label distribution for existing class prior based re-balance strategies. Extensive experiments on long-tailed decentralized datasets demonstrate the effectiveness of our method, showing superior performance to state-of-the-art methods.