Federated learning (FL) is a distributed model training framework that allows multiple clients to collaboratively train a global model in an edge computing (EC) environment while preserving the privacy of clients’ local data. However, federated learning in edge networks often faces challenges such as resource constraints and data heterogeneity, also known as non-independent and identically distributed (non-IID) data, which significantly degrade model training performance. To address these challenges, this study proposes an efficient federated learning framework—FedAlt, aiming to enhance model training performance (e.g., test accuracy) in edge networks while reducing resource consumption. FedAlt builds upon the classic federated learning algorithm FedAvg by incorporating block-wise multi-output and self-knowledge distillation techniques. These enhancements enable clients to more effectively absorb information from the model’s representational layers during local training, mitigating the negative impact of non-IID data on model training. Specifically, the model is divided into multiple consecutive blocks, and at the start of each global training round, the server sends only the initial blocks of the global model to the clients, reducing communication overhead. Clients then combine the global model with their local models and use self-knowledge distillation techniques to absorb information from the model’s representational layers, addressing data heterogeneity challenges. Moreover, considering that communication overhead increases with the number of transmitted model blocks, adaptive algorithms are designed for both the server and client sides: the model block distribution algorithm and the block-wise multi-output regularization algorithm. These algorithms dynamically adjust the number of distributed model blocks based on the client’s data distribution, computational capacity, and communication capabilities. Extensive experimental results show that, compared to existing methods, FedAlt improves average test accuracy by approximately 2.64% under limited communication bandwidth conditions.