  Federated learning encounters substantial challenges with heterogeneous data,
leading to performance degradation and convergence issues. While considerable
progress has been achieved in mitigating such an impact, the reliability aspect
of federated models has been largely disregarded. In this study, we conduct
extensive experiments to investigate the reliability of both generic and
personalized federated models. Our exploration uncovers a significant finding:
\textbf{federated models exhibit unreliability when faced with heterogeneous
data}, demonstrating poor calibration on in-distribution test data and low
uncertainty levels on out-of-distribution data. This unreliability is primarily
attributed to the presence of biased projection heads, which introduce
miscalibration into the federated models. Inspired by this observation, we
propose the "Assembled Projection Heads" (APH) method for enhancing the
reliability of federated models. By treating the existing projection head
parameters as priors, APH randomly samples multiple initialized parameters of
projection heads from the prior and further performs targeted fine-tuning on
locally available data under varying learning rates. Such a head ensemble
introduces parameter diversity into the deterministic model, eliminating the
bias and producing reliable predictions via head averaging. We evaluate the
effectiveness of the proposed APH method across three prominent federated
benchmarks. Experimental results validate the efficacy of APH in model
calibration and uncertainty estimation. Notably, APH can be seamlessly
integrated into various federated approaches but only requires less than 30\%
additional computation cost for 100$\times$ inferences within large models.
