  Federated learning learns from scattered data by fusing collaborative models
from local nodes. However, the conventional coordinate-based model averaging by
FedAvg ignored the random information encoded per parameter and may suffer from
structural feature misalignment. In this work, we propose Fed2, a
feature-aligned federated learning framework to resolve this issue by
establishing a firm structure-feature alignment across the collaborative
models. Fed2 is composed of two major designs: First, we design a
feature-oriented model structure adaptation method to ensure explicit feature
allocation in different neural network structures. Applying the structure
adaptation to collaborative models, matchable structures with similar feature
information can be initialized at the very early training stage. During the
federated learning process, we then propose a feature paired averaging scheme
to guarantee aligned feature distribution and maintain no feature fusion
conflicts under either IID or non-IID scenarios. Eventually, Fed2 could
effectively enhance the federated learning convergence performance under
extensive homo- and heterogeneous settings, providing excellent convergence
speed, accuracy, and computation/communication efficiency.
