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Federated Learning (FL) is a distributed machine learning approach to learnmodels on decentralized heterogeneous data, without the need for clients toshare their data. Many existing FL approaches assume that all clients haveequal importance and construct a global objective based on all clients. Weconsider a version of FL we call Prioritized FL, where the goal is to learn aweighted mean objective of a subset of clients, designated as priority clients.An important question arises: How do we choose and incentivize well aligned nonpriority clients to participate in the federation, while discarding misalignedclients? We present FedALIGN (Federated Adaptive Learning with Inclusion ofGlobal Needs) to address this challenge. The algorithm employs a matchingstrategy that chooses non priority clients based on how similar the models lossis on their data compared to the global data, thereby ensuring the use of nonpriority client gradients only when it is beneficial for priority clients. Thisapproach ensures mutual benefits as non priority clients are motivated to joinwhen the model performs satisfactorily on their data, and priority clients canutilize their updates and computational resources when their goals align. Wepresent a convergence analysis that quantifies the trade off between clientselection and speed of convergence. Our algorithm shows faster convergence andhigher test accuracy than baselines for various synthetic and benchmarkdatasets.

Utilizing Free Clients in Federated Learning for Focused Model Enhancement