In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. However, these methods impose limitations on the ability to explore superior local models and ignore the valuable information in historical models. Besides, although the up-to-date representation method simultaneously concerns the global and historical local models, it suffers from unbearable computation cost. To accelerate convergence with low resource consumption, we innovatively propose a model regularization method named FedTrip, which is designed to restrict global-local divergence and decrease current-historical correlation for alleviating the negative effects derived from data heterogeneity. FedTrip helps the current local model to be close to the global model while keeping away from historical local models, which contributes to guaranteeing the consistency of local updates among clients and efficiently exploring superior local models with negligible additional computation cost on attaching operations. Empirically, we demonstrate the superiority of FedTrip via extensive evaluations. To achieve the target accuracy, FedTrip outperforms the state-of-the-art baselines in terms of significantly reducing the total overhead of client-server communication and local computation.

翻译：在联邦学习场景中，地理上分布的客户端协作训练全局模型。客户端间的数据异质性显著导致模型更新不一致，从而明显减缓模型收敛速度。为缓解此问题，许多方法采用正则化项来缩小客户端本地模型与服务器端全局模型之间的差异。然而，这些方法限制了对更优本地模型的探索能力，且忽略了历史模型中的有价值信息。此外，虽然最新表示方法同时关注全局模型和历史本地模型，但存在难以承受的计算开销。为在低资源消耗下加速收敛，我们创新性地提出了一种名为FedTrip的模型正则化方法，旨在约束全局-本地差异并降低当前-历史相关性，从而缓解数据异质性带来的负面影响。FedTrip促使当前本地模型靠近全局模型，同时远离历史本地模型，这有助于保证客户端间本地更新的一致性，并以可忽略的附加计算开销高效探索更优本地模型。通过大量实验评估，我们实证证明了FedTrip的优越性。在达到目标精度时，FedTrip在显著降低客户端-服务器通信总开销和本地计算量方面优于最先进的基线方法。