Author name cluster

Hyuk Lim

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2022 Short Paper

AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract)

Suji Jang
Hyuk Lim

Federated learning (FL) performs the global model updating in a synchronous manner in that the FL server waits for a specific number of local models from distributed devices before computing and sharing a new global model. We propose asynchronous federated learning (AsyncFL), which allows each client to continuously upload its model based on its capabilities and the FL server to determine when to asynchronously update and broadcast the global model. The asynchronous model aggregation at the FL server is performed by the Boyer–Moore majority voting algorithm for the k-bit quantized weight values. The proposed FL can speed up the convergence of the global model learning early in the FL process and reduce data exchange once the model is converged.

PDF Details

AAAI Conference 2022 Short Paper

FedCC: Federated Learning with Consensus Confirmation for Byzantine Attack Resistance (Student Abstract)

Woocheol Kim
Hyuk Lim

In federated learning (FL), a server determines a global learning model by aggregating the local learning models of clients, and the determined global model is broadcast to all the clients. However, the global learning model can significantly deteriorate if a Byzantine attacker transmits malicious learning models trained with incorrectly labeled data. We propose a Byzantine-robust FL algorithm that, by employing a consensus confirmation method, can reduce the success probability of Byzantine attacks. After aggregating the local models from clients, the proposed FL server validates the global model candidate by sending the global model candidate to a set of randomly selected FL clients and asking them to perform local validation with their local data. If most of the validation is positive, the global model is confirmed and broadcast to all the clients. We compare the performance of the proposed FL against Byzantine attacks with that of existing FL algorithms analytically and empirically.

PDF Details