Decentralized learning enables collaborative machine learning with enhanced privacy by allowing participants to train models locally and share updates for aggregation instead of sharing raw data. However, such systems are vulnerable to poisoning attacks that may compromise the learning process. This threat becomes even more severe when combined with sybil attacks, where adversaries contribute numerous malicious updates with minimal effort. To overcome this challenge, particularly in the permissionless setup, we propose SyDeLP, a blockchain-enabled protocol for decentralized learning. SyDeLP integrates byzantine tolerant aggregation for poisoning mitigation with a Verifiable Delay Function to counter sybil attacks requiring Proofs of Work (PoW) to participate. Honest behavior is incentivized by dynamically reducing PoW difficulty, decreasing the computational burden for honest nodes over time. Empirical evaluations conducted on a benchmark dataset across three types of poisoning attacks demonstrate that SyDeLP consistently outperforms existing solutions in terms of resilience.