SPADE-KD: Peer-to-Peer Collaborative Learning under Heterogeneous Settings
Peer-to-peer collaborative learning is an emerging distributed deep learning paradigm that eliminates the need for a central aggregator and disseminator, avoiding the single-point-of-failure and bottleneck issues they can bring. Many such approaches use parameter averaging, requiring clients to have homogeneous model architectures, whereas others use knowledge distillation as an alternative while leaning on shared public data or centralized control infrastructure. Many of these approaches also make a one-size-fits-all assumption, i.e., jointly training a single, globally optimal solution for all clients, when in reality, clients' underlying data distributions might differ such that a glob...
| Authors | Harikrishna Kuttivelil, Katia Obraczka |
|---|---|
| Primary category | cs.LG |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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