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Found 4 papers, 0 papers with code
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
The former indicates that representation collapse in local model will subsequently impact the global model and other local models.
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning
Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients.
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach
Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step.
